Machine Learning + X seminars are held weekly.
Selected presentations from past seminars are posted below.
- October 22 Recording
- October 22, 2021: Domain Adaptation Methods for Deep Regression by Xinyang Chen, Tsinghua University
- October 22, 2021: Data-driven computation for invariant probability measures by Yao Li, UMass Amherst
- October 15 Recording
- October 15, 2021: Enabling Rapid Meshless Multiphysics With Hybrid Data-Driven Projection-Tree Reduced-Order Modeling by Steven Rodriguez, U.S. Naval Research Laboratory
- October 15, 2021: Optimal renormalization of multiscale systems by Panos Stinis, PNNL
- October 8 Recording
- October 8, 2021: Galerkin Transformer by Shuhao Cao, WUSTL
- October 8, 2021: Operator Networks with Predictive Uncertainty for Partial Differential Equations with Inhomogeneous Boundary Conditions by Nickolas D Winovich, Purdue
- October 1 Recording
- October 1, 2021: GrADE: A graph-based data-driven solver for time-dependent nonlinear partial differential equations by Souvik Chakraborty, IIT Delhi
- October 1, 2021: A spectral approach for time-dependent PDEs using machine-learned basis functions by Saad Qadeer, Pacific Northwest National Laboratory and Brek Meuris, University of Washington
- September 24 Recording
- September 24, 2021: Thoughts on Deep Learning by Ron DeVore, Texas A&M University
- September 24, 2021: Whirlwood tour of linearization and beyond in deep learning by Jason Lee, Princeton University
- September 17 Recording
- September 17, 2021: Neural Nets and Numerical PDEs by Zhiqiang Cai, Purdue University
- September 17, 2021: GFINNs: GENERIC Formalism Informed Neural Networks for Deterministic and Stochastic Dynamical Systems by Zhen Zhang, CRUNCH Group
- September 10 Recording
- Septmeber 10, 2021: Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems by Jeremy Yu, St. Mark's School of Texas
- September 10, 2021: Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models by Katiana Kontolati, Johns Hopkins University
- September 3 Recording
- September 3, 2021: Systems biology informed deep learning applied to a red blood cell clearance model by Achilles Gatsonis, Brown University
- September 3, 2021: High order finite element methods with extra smoothness by Charles Parker, Brown University
- August 27 Recording
- August 27, 2021: Deep Learning for Turbulent Flows and Physics-Informed-Neural-Networks (PINNs) Applications by Dr. Ricardo Vinuesa & Hamidreza Eivazi, KTH Royal Institute of Technology
- August 27, 2021: Paper Review) PhyCRNet: Physics-informed Convolutional-Recurrent Network for Solving Spatiotemporal PDEs by Varun Kumar
- August 20 Recording
- August 20, 2021: A Crash Course on Reinforcement Learning by Kamaljyoti Nath, Brown University
- August 20, 2021: Data-driven discovery of governing equations for fluid dynamics based on molecular simulation by Xuhui Meng, Brown University
- August 13 Recording
- August 13, 2021: PDE-Constrained Models with Neural Network Terms: Optimization and Global Convergence by Prof. Konstantinos Spiliopoulos, Boston University
- August 13, 2021: Machine Learning for Solid Mechanics by Prof. Grace Gu, UC Berkeley
- August 6 Recording
- August 6, 2021: Data-driven modeling for unsteady aerodynamics and aeroelasticity by Jiaqing Kou, Technical University of Madrid
- August 6, 2021: Explore missing flow dynamics by physics-informed deep learning: the parametrised governing system by Patricio Clark Di Leoni
- July 30 Recording
- July 30, 2021: Machine Learning Approaches for Thermodynamic and Kinetic Analysis of Molecular Systems by Hao Wu, Tongji University, China
- July 30, 2021: Nonlocal Kernel Network (NKN): a Stable and Resolution-Independent Deep Neural Network by Yue Yu, Lehigh University
- July 23 Recording
- July 23, 2021: Partition of Unity Networks for Deterministic and Probabilistic Regression by Mamikon Gulian, Sandia National Laboratories
- July 23, 2021: Neural Network Method for solving parabolic two-temperature microscale heat conduction in double-layered thin films exposed to ultrashort-pulsed lasers by Aniruddha Bora, Louisiana tech University
- July 16 Recording
- July 16, 2021: Paper Review - Simulating Continuum Mechanics with Multi-Scale Graph Neural Networks by Presented by Shengze Cai, CRUNCH Group
- July 16, 2021: Paper Review - Machine Learning the Kinematics of Spherical Particles in Fluid Flow by Leo Liu, CRUNCH Group
- July 9 Recording
- July 9, 2021: Researches on Molecular Geometry and Related Applications by Sheng Gui, Chinese Academy of Sciences
- July 2 Recording
- July 2, 2021: Artificial Intelligence for Engineering Design and Computational Mechanics by Ramin Bostanabad, University of California, Irvine
- July 2, 2021: Learning Functional Priors and Posteriors from Data and Physics by Xuhui Meng, CRUNCH Group
- June 25 Recording
- June 25, 2021: Transfer learning based multi-fidelity physics informed deep neural network by Souvik Chakraborty, Indian Institute of Technology
- June 25, 2021: On optimization and generalization in deep neural networks by Kenji Kawaguchi, Harvard University
- June 18 Recording
- June 18, 2021: Recent Developments in the Mathematics of Neural Nets by Anirbit Mukherjee, University of Pennsylvania
- June 18, 2021: Predicting High-Stress Regions Inside Microstructure Using Deep Learning by Ankit Shrivastava, Carnegie Mellon University
- June 11 Recording
- June 11, 2021: Physics Embedded Machine Learning Methods in Hierarchical Constitutive And Damage Modeling of Metals and Composites by Somnath Ghosh, Johns Hopkins University
- June 11, 2021: Can stable and accurate neural networks be computed? On the barriers of deep learning and Smale's 18th problem by Matthew Colbrook, University of Cambridge
- June 4 Recording
- June 4, 2021: Data-Driven Methods of Accelerating Physical Simulations and Their Applications by Youngsoo Choi, Lawrence Livermore National Laboratory
- June 4, 2021: Physics-infused Machine Learning and Evolutionary Learning Approaches to Design Intelligent Robotic Systems by Amir Behjat, University at Buffalo
- May 28 Recording
- May 28, 2021: Parametric deep energy approach for elasticity accounting strain gradient effects (Paper review) by
Somdatta Goswami, Crunch Group
-
May 28, 2021: Deep physical neural networks enabled by a backpropagation algorithm for arbitrary physical systems (Paper review) by Liu Yang, Crunch Group
- May 21 Recording
- May 21, 2021: Self-Adaptive Physically-Informed Neural Networks by Ulisses M. Braga-Neto, Texas A&M University
- May 21, 2021: MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks Presented by Zongren Zou, CRUNCH Group
- May 14 Recording
- May 14, 2021: Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks by N. Sukumara/Ankit Srivastava, Illinois Institute of Technology
- May 14, 2021: Physics-informed Learning for Data-driven Discovery of Governing Laws by Hao Sun, Northeatern University
- May 7 Recording
- May 7, 2021: Physics-Constrained Reservoir-Computing for Turbulence and Chaotic Learning by Luca Magri, Imperial College London
- May 7, 2021: Hierarchical Multi-Instance Learning: Theory, Some Applications, and Benefits by Tomas Pevny, Czech Technical University in Prague
- April 30 Recording
- April 30, 2021: Physics-Informed Neural Networks for Elliptic Partial Differential Equations on 3D Manifolds by Cem Celik, Brown University
- April 30, 2021: A Caputo fractional derivative-based algorithm for optimization by Yeonjong Shin, Brown University
- April 23 Recording
- April 23, 2021: Solving and Learning Nonlinear PDEs with Gaussian Processes by Bamdad Hosseini, Caltech
- April 23, 2021: A Machine-Learning Method for Time-Dependent Wave Equations over Unbounded Domains by Ameya Jagtap, Brown University
- April 16 Recording
- April 16, 2021: Distributional Offline Continuous-Time Reinforcement Learning with Neural Physics-Informed PDEs by Igor Halperin, AI AM Research, Fidelity Investments
- April 16, 2021: Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets by Somdatta Goswami, Brown University
- April 9 Recording
- April 9, 2021: Accelerating kinetic simulations of magnetic-confinement fusion devices utilizing encoder-decoder neural networks to numerically solve the nonlinear Fokker-Planck collision operator by Michael Churchill, Princeton Plasma Physics Laboratory
- April 9, 2021: A New Stochastic Dynamic Graph Embedding Model - DynG2G by Apoorva Vikram Singh
- April 2 Recording
- April 2, 2021: Hyper-differential sensitivity analysis for control under uncertainty of aerospace vehicles by Bart van Bloemen Waanders, Sandia National Laboratory
- April 2, 2021: Incorporating Physical Principles into Deep Dynamics Models by Rose Yu, Assistant Professor, Computer Science and Engineering, UC San Diego
- March 26 Recording
- March 26, 2021: Learning emergent PDEs in a learned emergent space by Prof. Ioannis G. Kevrekidis, Johns Hopkins University
- March 26, 2021: Dissertation Defense - Generative Adversarial Networks for Physics-Informed Learning by Liu Yang
- March 19 Recording
- March 19, 2021: Convergence Analysis of Numerical PDEs by Neural Network Functions by Prof. Jinchao Xu, The Pennsylvania State University
- March 19, 2021: A Simple Modeling Framework For Prediction In The Human Glucose-Insulin System by Zhen Zhang, CRUNCH
- March 12 Recording
- March 12, 2021: Orbital Dynamics of Binary Black Hole Systems can be Learned from Gravitational Wave Measurements by Brendan Keith
- March 12, 2021: Local error quantification for Neural Network Differential Equation solvers by Beichuan Deng, Worcester Polytechnic Institute
- March 5 Recording
- March 5, 2021: Deep neural network surrogates for non-smooth quantities of interest in shape uncertainty quantification by Laura Scarabosio, Radboud University, The Netherlands
- March 5, 2021: SIAG CSE Early Career Prize Lecture: Bridging Physical Models and Observational Data with Physics-Informed Deep Learning by Paris Perdikaris, University of Pennsylvania, U.S.
- March 5, 2021: SIAM-ACM Prize in Computational Science and Engineering Lecture: DeepOnet: Learning Linear, Nonlinear and Multiscale Operators Using Deep Neural Networks Based on the Universal Approximation Theorem of Operators by George E. Karniadakis, Brown University, U.S.
- February 26 Recording
- February 26, 2021: Theoretical Guarantees of Machine Learning Methods for Solving High Dimensional PDEs by Yulong Lu , University of Massachusetts, Amherst
- February 26, 2021: Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks by Zongren, Zou CRUNCH
- February 19 Recording
- February 19, 2021: Optimization and Learning With Nonlocal Calculus by Sriram Nagaraj, Quantitative Specialist at Federal Reserve Bank of Atlanta
- Fecruary 19, 2021: Learning Poisson systems and trajectories of autonomous systems via Poisson neural networks by Zhen Zhang, CRUNCH
- February 12 Recording
- February 12, 2021: On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks by Sifan Wang
- February 12, 2021: Physics-informed neural networks with hard constraints for inverse design by Lu Lu
- February 5 Recording
- February 5, 2021: Deep evidential classification/regression by Apostolos Psaros
- February 5, 2021: FBSDE based neural network algorithms for high-dimensional quaslinear parabolic PDEs by Wenzhong Zhang, Southern Methodist University
- Janaury 29 Recording
- Janaury 29, 2021: Deep reconstruction of strange attractors from time by Ehsan Kharazmi
- January 29, 2021: Integrating Machine Learning & Multiscale Modeling in Biomedicine by Lu Lu, Department of Mathematics, Massachusetts Institute of Technology
- Janaury 22 Recording
- January 22, 2021: Learning Local Conservation Laws via Inversion by Jong-Hoon Ahn
- January 22, 2021: Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting by Zhen Zhang
- Janaury 22, 2021: Adversarial Sparse Transformer for Time Series Forecasting by Jeremy Chen
- January 15 Recording
- January 15, 2021: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation by Mengjia Xu
- January 15, 2021: Towards a mathematical understanding of modern machine learning: theory and algorithm by Yeonjong Shin
- January 8 Recording
- January 8, 2021: Control volume PINNs: a method for solving inverse problems with hyperbolic PDEs by Patel, Ravi Ghanshyam, Sandia national laboratories
- January 8, 2021: Gaussian Processes Kernels and Neural Tangent Kernel of Deep Neural Networks by Jiaxi Zhao, Stony Brook University
- December 18 Recording
- December 18, 2020:Learning in the Frequency Domain by Ehsan Kharazmi
- December 18, 2020:Towards NNGP-guided Neural Architecture Search by Liu Yang
- December 11 Recording
- December 11, 2020: Information transfer in multi-task learning by Hongyang Zhang, Assistant Professor of Computer Science at Northeastern University
- December 11, 2020: Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical
Kinetics by Sumit Vashishtha
- December 4 Recording
- December 4, 2020: Machine Learning for Inverse Problems in Computational Engineering by Kailai Xu, Institute for Computational and Mathematical Engineering, Stanford University
- December 4, 2020: Generalization effects of linear transformation in data augmentation by Sen Wu, Computer Science Department, Stanford University
- December 4, 2020: Upscaling Transport and Reactions in Tissues: Nonlinear Closure via Deep Learning by Ehsan Taghizadeh, School of Chemical, Biological, and Environmental Engineering Oregon State University
- November 27 Recording
- November 27, 2020: A Point-Cloud Deep Learning Framework for Prediction of Fluid Flow Fields on Irregular Geometries by Ali Kashefi
- November 27, 2020: Variable-Order Approach to Nonlocal Elasticity: Theoretical Formulation and Order Identification via Deep Learning Techniques by Enrui Zhang
- November 20 Recording
- November 20, 2020: A Combinatorial Perspective on Transfer Learning https://arxiv.org/pdf/2010.12268.pdf by Somdatta Goswami
- November 20, 2020: Implicit Neural Representations with Periodic Activation Functions https://arxiv.org/pdf/2006.09661.pdf by Ameya Jagtap
- November 13 Recording
- November 13, 2020: Historic First: Tracking the Global Pandemic in Real-time by Ensheng (Frank) Dong , Department of Civil and Systems Engineering, Johns Hopkins University
- November 13, 2020: Fourier Neural Operator for Parametric Partial Differential Equations by Zongyi Li, Caltech
- November 6 Recording
- November 6, 2020: Spotting hidden weakness of constitutive laws with multi-agent deep reinforcement learning by Steve WaiChing Sun, associate professor, Department of Civil Engineering and Engineering Mechanics Columbia University, New York, USA
- November 6, 2020: Introduction of CONVERGE CFD Software by Dr. Daniel Lee, Convergent Science
- October 30, 2020 Recording
- October 30, 2020: Solving high-dimensional stochastic partial differential equations with physics-informed neural networks by Ilias Bilionis, Associate Professor, School of Mechanical Engineering, Purdue University
- October 30, 2020: Mobility Evaluation for Hybrid Robot Motion on Deformable Terrain via Physics-Based and Data-Driven Modeling Approach by Guanjin Wang , Mechanical engineering at University of Maryland
- October 30, 2020: Ab initio solution of the many-electron Schrödinger equation with deep neural networks by Liu Yang
- October 23, 2020: Multi-scale Deep Neural Network (MscaleDNN) Methods for Oscillatory Stokes Flows in Complex Domains by Wei Cai, Southern Methodist University
- October 23, 2020: Data-Driven Multi Fidelity Physics-Informed Constitutive Meta-Modeling of Complex Fluids by Mohammadamin Mahmoudabadbozchelou, Northeastern University
- October 16 Recording
- October 16, 2020: Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC Via Variance Reduction by Wei Deng, Purdue University
- October 16, 2020: Overcoming the curse of dimensionality for some Hamilton--Jacobi partial differential equations via neural network architectures by Tingwei Meng
- October 9 Recording
- October 9, 2020: Solving PDE related problems using deep-learning by Adar Kahana, Tel Aviv University
- October 9, 2020: Uncertainty in Neural Networks: Approximately Bayesian Ensembling by Liu Yang
- October 2 Recording
- October 2, 2020: From PINNs to DeepOnets: Approximating functions, functionals, and operators using deep neural networks for diverse applications by George Karniadkais, Brown University
- October 2, 2020: COVID-19 dynamics across the US: A deep learning study of human mobility and social behavior by Mohamed Aziz Bhouri, University of Pennsylvania
- September 25 Recording
- September 25, 2020: Integrating Physics-Based Modeling with Machine Learning: A Survey by Jared Willard, University of Minnesota
- September 25, 2020: Learning Solutions to Differential Equations using LS-SVM by Simin Shekarpaz
- September 18 seminar Recording
- September 18, 2020: Designing complex architectured materials with generative adversarial networks by Minglang Yin
- September 18, 2020: Shallow PINNs using Levenberg-Marquardt algorithm for optimization by Gaurav Kumar Yadav
- September 18, 2020: Background and some practical applications of seq2seq modeling by Fumi Honda and Jeremy Chen
- September 11 Seminar Recording
- September 11, 2020: Improved Architecture for Distributed PINNs by Sreehari M
- September 11, 2020: Shallow PINNs using Levenberg-Marquardt algorithm for optimization by Gaurav Kumar Yadav
- September 11, 2020: The effectiveness of PINNs for solving inverse heat transfer problems by VIVEK OOMMEN
- September 11, 2020: Sequence-to-sequence prediction of spatiotemporal systems by Zhen Zhang
- September 4, 2020: How to Deal with Imbalanced Dataset? by Yixiang Deng
- September 4, 2020: Thermodynamics-based Artificial Neural Networks for constitutive modeling by Enrui Zhang
- August 28, 2020: Notes on Bayesian deep learning by Apostolos Psaros
- August 28, 2020: GAN-BERT: Generative Adversarial Learning for Robust Text Classification with a Bunch of Labeled Examples by Liu Yang
- August 21, 2020: When and Why PINNS fail to train: A Neural Tangent Kernel Perspective by Paris Perdikaris
- August 21, 2020: The Computational Limits of Deep Learning by Khemraj Shukla
- August 14, 2020: Loss landscape: SGD can have a better view than GD by Yeonjong, Shin
- August 14, 2020: SIAN: software for structural identifiability analysis of ODE models by Zhen Zhang
- August 14, 2020: The Computational Limits of Deep Learning
by Khemraj Shukla
- August 7, 2020: Discovering Reinforcement Learning Algorithms by Sumit Vashishtha
- August 7, 2020: Physics-Informed Neural Network Framework for PDEs on 3D Surfaces by Zhiwei Fang
- July 31, 2020: Output-Weighted Importance Sampling for Bayesian Experimental Design and Uncertainty Quantification by Antoine Blanchard, MIT
- July 31, 2020: Development of Interatomic Potential Energy Surfaces Based on ab initio Electronic Structure Methods and Neural Networks for Molecular Dynamics Simulations by Milind Malshe , Georgia Institute of Technology
- July 24, 2020: Error estimates for PINNs by Siddhartha Mishra, ETH Zurich, Switzerland
- July 24, 2020: Convergence of PINNs and hp-VPINNs for advection-diffusion-reactions equations by Zhongqiang Zhang (Handy), WPI
- July 17, 2020: Data-Driven and Physics-Constrained Deep Learning for Transport Phenomena in Heterogeneous Media by Haiyi Wu, Virginia Tech
- July 17, 2020: Hydrodynamics of driven and active colloids at fluid interfaces by Nicholas Chisholm, University of Pennsylvania
- July 10, 2020: Data-Driven Continuum Dynamics via Transport-Teleport Duality by Jong-Hoon Ahn, Purdue University
- July 10, 2020: New Overlapping Finite Elements and Their Application in the AMORE Paradigm by Junbin Huang, MIT
- July 3, 2020: Invnet: Encoding Constraints in Generative Models by Ameya Joshi
- July 3, 2020: Learning Energy-based Model with Flow-based Backbone by Neural Transport MCMC by Minglang Yin
- June 26, 2020: Neural Tangent Kernel: Convergence and Generalization in Neural Networks by Guofei Pang
- June 26, 2020: Physics Informed Reinforcement Learning (PIRL): Possibilities and Promises by Sumit Vashishtha
- June 26, 2020: Sample-based Forward and Inverse Fokker-Planck Equation Solver with Physics-informed Neural Networks by Xiaoli Chen
- June 19, 2020: Deep learning of free boundary and Stefan problems by Sifan Wang, Upenn
- June 19, 2020: Physics Informed Reinforcement Learning (PIRL): Possibilities and Promises by Sumit Vashishtha
- June 12, 2020: Explaining Neural Networks by Decoding Layer Activations by Xuhui Meng
- June 12, 2020: Deep Learning for Symbolic Mathematics by Zhen Zhang
- June 5, 2020: Introduction to SimNet by Sanjay Choudhry, Nvidia
- June5, 2020: Complexity and the Dunbar Hypothesis by Bruce J. West, ST-Senior Scientist Mathematics, Army Research Office
- May 29, 2020: DiffTaichi: Differentiable Programming for physical simulation by Leonard Gleyzer
- May 29, 2020: Data-driven Fractional Modeling for Anomalous Transport and Turbulent Flows by Mehdi Samiee
- May 22, 2020: Double-descent phenomenon in modern machine learning by Lu Lu
- May 22, 2020: Phase field modeling of fracture with isogeometric analysis and machine learning methods by
Somdatta Goswami, Bauhaus University Weimar, Germany
-
May15, 2020: Automating data augmentation: practice, theory and future direction by Mengjia Xu
- May 15, 2020: Vision: Digital Twin for Additive Manufacturing by Henning Wessels, Institute of Continuum Mechanics, Leibniz University Hannover
- May 8, 2020: Meta-Learning in Neural Networks: A Survey by Zongren Zou
- May 8, 2020: Transfer learning enhanced physics informed neural network for phase-field modeling of fracture by Xiaoning Zheng
- May 1, 2020: PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain by Jianxun Wang
- May 1, 2020: Bayesian differential programming for robust systems identification under uncertainty by Paris Perdikaris
- May 1, 2020: Meta-Learning in Neural Networks: A Survey by Zongren Zou
- April 24, 2020: Automatic identification of the shape of retinal microaneurysms from retinal images by Qian Zhang
- April 24, 2020: Machine learning for active matter by Chensen Lin
- April 24, 2020: Discussion of "learning and solving" by Prof. Yannis Kevrekidis
- April 17, 2020: On the Convergence and Generalization of Physics Informed Neural Networks by Yeonjong Shin
- April 17, 2020: Path integrals and sparse representations in computational stochastic dynamics by Apostolos Psaros
- April 17, 2020: Discussion of "learning and solving" by Prof. Yannis Kevrekidis
- April 10, 2020: Illustration of the benefits of wearing face mask in public during the COVID-19 pandemic using hidden fluid mechanics (HFM) by Shenze Cai
- April 10, 2020: The Reconstruction and Prediction Algorithm of the Fractional TDD for the Local Outbreak of COVID-19 by Zhiping Mao
- April 10, 2020: Real-valued (medical) times series generation with recurrent conitional gans by Liu Yang
- April 3, 2020: Symplectic networks: Intrinsic structure-preserving networks for identifying Hamiltonian systems by Zhen Zhang
- April 3, 2020: Data-driven stochastic modeling of reaction initiation in granular energetic materials by Joseph Bakarji
- March 27, 2020: On the use of machine learning to investigate the fracture toughness of ceramic nanocomposites by Christos E. Athanasiou
- March 27, 2020: Restoring chaos using deep reinforcement learning by Sunit Vashishtha
- March 20, 2020: Deep Variartional Information Bottleneck by Liu Yang
- March 20, 2020: A deep surrogate approach to efficient Bayesian inversion in PDE and integral equation models by Xuhui Meng
- March 20, 2020: The Case for Bayesian Deep Learning by Xuhui Meng
- March 13, 2020: Integrating Physics-Based Modeling with Machine Learning: A Survey by Yosuke Hasegawa
- March 13, 2020: Analyzing Inverse Problems with Invertible Neural Networks by Minglang Yin
- February 28, 2020: A study of the sungle-layer ReLU neural network by Sheng Chen
- February 28, 2020: Identifying Critical Neurons in ANN Architectures using Integer Programming by Zongren Zou
- February 14, 2020: VarNet: Variational Neural Networks for the Solution of Partial Differential Equations by Ehsan Kharazmi
- February 14, 2020: Discovery of Dynamics using Linear Multistep Methods by Zhen Zhang
- February 14, 2020: A deep learning approach for efficiently and accurately evaluating the flow field of supercritical airfoils by Ameya Jagtap
- February 7, 2020: Deep Learning in turbulent convection networks by Yosuke Hasegawa
- January 31, 2020: Preventing Undesirable Behavior of Intelligent Machines by Yixiang Deng
- January 24, 2020: Data-assisted reduced-order modeling of extreme events in complex dynamical systems by Zhen Zhang
- January 24, 2020: Paper review: Learning functionals via LSTM neural networks for predicting vessel dynamics in extreme sea states by Zhen Zhang
- January 24, 2020: Robust Training and Initialization of Deep Neural Networks: An Adaptive Basus Viewpoint by Ehsan Kharazmi
- January 24, 2020: Introduction to the Dirichlet Procecss by Liu Yang
- January 3, 2020: DBSN: Measuring Uncertainty through Bayesian Learning of Deep Neural Network Structures by Xuhui Meng
- January 3, 2020: Antisymmetricrnn: A Dynamical system view on recurrent neural networks by Zhen Zhang
- December 27, 2019: A machine learning framework for solving high-dimensional mean field game and mean field control problemsing by Liu Yang
- December 27, 2019: Learning to Reconstruct Crack Profiles for Eddy Current Nondestructive Testing by Enrui Zhang
- December 19, 2019: Introducting AdaNet: Fast and Flexible AutoML with Learning Guarantees (Tutorial) and AdaNet: Adaptive Structural Learning of Artifical Neural Network by Guofei Pang and Liu Yang
- December 13, 2019: Turbulance Control - Better, Faster and Easier with Machine Learning by Bernd R. Noack from LIMSI, CNRS, University Paris-Saclay, France; TU Berlin; TU Braunschweig & HITSZ
- December 13, 2019: Simulation of droplet using many-body dissipative particle dynamics and machine learning potentials for atomistic simulations by Chensen Lin
- December 6, 2019: A Few Ideas from Neurobiology for Unsupervised Learning by Dmitry Krotov & Leopold Grinberg from IBM Research
- November 22, 2019: The problem of inverse wave scattering: classical techniques and emerging approaches by L.D. Negro
- November 15, 2019: Scientific Machine Learning with domain awarness: Theory Algotithms & Software by Lu Lu
- November 15, 2019: Structure preserving schemes for complex nonlinear systems by Jie Shen
- November 8, 2020: Calibrating nonlocal diffusion and turbulence models using PINNs by G. pang
- November 1, 2019: 3D Multi Source Localisation of Underwater Objects using Artificial Lateral Lines and Convolutional Neural Networks by Cai, Shengze colllaborating with M. L. Yin
- November 1, 2019: Dimension redtion for increasing power in genomics by G. Darnell
- October 25, 2019: An entropy viscosity method for simulation of flows at high Reynolds number with applications from aerodynamics to chronic man's problem by Z. Wang
- October 25, 2019: Application of PINNs on flow estimation problems based on limited measurements by Z. lIU
- October 18, 2019: DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators by Lu Lu
- October 18, 2019: DIRECT SHAPE OPTIMIZATION THROUGH DEEP REINFORCEMENT LEARNING by Ameya Jagtap
- October 18, 2019: Replacing sea ics-wave interactions with superparameterization and machine learning by Christopher Horvat
- October 4, 2019: Benchmarking TPU, GPU, and CPU Platforms for Deep Learning by Khemraj Shukla
- October 4, 2019: Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning by Yang Liu
- Adjoint-based olfactory search algorithm in turbulent environments by Yosuke Hasegawa.
- September 27, 2019: Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet by Xuhui Meng
- September 27, 2019: Which Deep Learning Framework is Growing Fastest? TensorFlow vs. PyTorch, Minglang Yin
- September 13, 2019: An efficient spectral approximation to singular problems with one-point singularity by Sheng Chen
- September 13, 2019: An Energy Approach to the Solution of Partial Differential Equations in Computational Mechanics via Machine Learning: Concepts, Implementation and Applications by Enrui Zhang
- September 6, 2019: An empirical model of large batch training by Xiaowei Jin
- September 6, 2019: Accurate, large minibatch sgd: Training imagenet in 1 hour by Xiaowei Jin
- September 6, 2019: A new generation of PINN: Systems Biology Informed Deep Learning: Inferring Hidden Dynamics and Parameters by Alireza Yazdani
- August 30, 2019: Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry by Qian Zhang
- August 30, 2019: Physically informed artificial neural networks for atomistic modeling of materials by Qian Zhang
- August 30, 2019: Machine learning of coarse-grained molecular dynamics force fields by Yixiang Deng
- August 30, 2019: Boltzmann generators-sampling equilibrium states of many-body systems with deep learning by Yixiang Deng
- August 23, 2019: Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models by Gan Liu
- August 23, 2019: Deep-PIV: particle image velocimetry via deep learning techniques by Shengze Cai
-
August 16, 2019: Improving Simple Models with Confidence Profiles by Lu Lu
- August 16, 2019: Quantifying PINN performance on Chaotic ODEs (Mathieu's Equation) by George Karniadakis
- August 8, 2019: Data-driven modeling of stochastic systems with adversarial deep learning by Paris Perdikaris
- August 2, 2019: Highlights of Deep Learning for Science School by Guofei Pang and Lu Lu
-
July 12, 2019: Recurrent Neural Networks and reservoir computing for spatio-temporal forcasting of chaotic dynamics by Yan Liu
- June 21, 2019: Paper reviews: 1) Diagnosing Pregnancy Based on Wrist Pulse Wave, 2) Human Pulse Recognition based on Convolutional Neural Networks and 3) Wrist Pulse Signals Analysis based on Deep Convolutional Neural Network by Xiaoli Chen
- June 21, 2019: Deep learning observables in computational fluid dynamics by Ameya D. Jagtap
- June 14, 2019: Deep Learning for Ocean Remote Sensing: An Application of Convolutional Neural Networks for Super-Resolution on Satellite-Derived SST Data by Xiang Li
- June 14, 2019: Applications of Deep Learning to Ocean Data Inference and Subgrid Parameterization by Xiang Li
- June 14, 2019: The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies by Guofei Pang
- June 7, 2019: Can PINNs beat FWI by Yiran Xu
- June 7, 2019: Graph Embedding method for Network data analysis by Mengjia Xu.
- May 31, 2019: Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness. Pengzhan Jin
- May 31, 2019: MoGlow: Probabilistic and controllable motion synthesis using normalising flows by Liu Yang
- May 24, 2019: Super-resolution reconstruction of turbulent flows with machine learning by Xiaowei Jin
- May 24, 2019: Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam by Ehsan Kharazmi
- May 10, 2019: Bridging Finite Element and Machine Learning Modeling: Stress Prediction of Arterial Walls in Atherosclerosis by Xiaoning Zheng
- May 10, 2019: Deep relaxation: partial differential equations for optimizing deep neural networks by Yeonjong Shin
- May 3, 2019: Data driven nonlinear dynamical systems identification using multi-step CLDNNZhiping Mao
- Recent applications of neural networks in biological systems
- May 3, 2019: A synthetic turbulent inflow generator using machine learning by Fangying Song
- May 3, 2019: End-to-end differentiable learning of protein structure by Yixiang Deng
- May 3, 2019: Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells by Yixiang Deng
- April 26, 2019: Spectral Normalization for Generative Adversarial Networks Liu Yang
- April 26, 2019: Generative Modeling using the Sliced Wasserstein Distance by Liu Yang
- April 26, 2019: Sliced Wasserstein Generative Models by Liu Yang
- April 26, 2019: Understanding training and generalization in deep learning by Fourier analysis Xuhui Meng
- April 22, 2019: An analysis of training and generalization errors in shallow and deep networks by Hrushikesh Mhaskar
- April 22, 2019: Deep vs. Shallow Networks: an Approximation Theory Perspective by Hrushikesh Mhaskar
- April 19, 2019: Predicting the solutions of heterogeneous elliptic PDEs with a confidence interval by probabilistic convolutional neural networks by Guofei Pang
- April 19, 2019: Error bounds for approximations with deep ReLU neural networks in W^{s,p} norms by Mamikon Gulian
- April 19, 2019: Universal Function Approximation by Deep Neural Nets with Bounded Width and ReLU Activations by Mamikon Gulian
- April 12, 2019: Assessment of End-to-End and Sequential Data-driven Learning of Fluid Flows by Ameya Jagtap
- April 12, 2019: An introduction to the application of GANs in hydrogeology by Qiang Zheng
- April 12, 2019: Physics-Informed Neurak Networkd (PINNs) for high speed flows by Zhiping Mao
- April 5, 2019: Learning Noise-Invariant Representations for Robust Speech Recognition by Zhiping Mao and Xuhui Meng
- April 5, 2019: Stochastic modeling of data-driven complex systems using machine learning tools by Dongkun Zhang
- March 29, 2019: Boost your research through SciCoNet and SumsJob by Lu Lu
- March 22, 2019: Deep Fluids: A Generative Network for Parameterized Fluid Simulations by Minglang Yin
- March 22, 2019: Deep Potential: a general representation of a many-body potential energy surface by Zhen Li
- March 15, 2019: Optimal approximation of continuous functions by very deep ReLU networks by Mamikon Gulian
- March 15, 2019: Coarse Independent Particles using Mori-Zwanzig Theory by Mark Thachuk
- March 1, 2019: Numerical solution of some evolutionary partial differential equations by Ameya Jagtap
- March 1, 2019: Artificial Neural Networks Trained Through Deep Reimforcement Learning Discover Control Strategies for Active Flow Control by Ameya Jagtap
- February 22, 2019: An inverse problem framework for extracting phonon properties from thermal spectroscopy measurments by Mojtaba Forghani, MIT
- February 22, 2019: Elimination of All Bad Local Minima in Deep Learning by Yeonjong Shin
-
A review of definitions of fractional derivatives and other operators by Ehsan Kharazmi
-
-
February 8, 2019: Vascular Network of Zebrafish Brain by Yosuke Hasegawa
-
Prediction of atomization energy using graph kernel and active learning by Dr. Yu-Hang Tang, Lawrence Berkeley National Laboratory
- January 25 2019: Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks by Guofei Pang
- January 25, 2019: Introduction to ResNet by Lu Lu
- January 11, 2019: pH-responsive polymer-grafted nanoparticles: From colloidal monolayer to Pickering emulsion by Shiyi Qin, State University if NY at Binghamton
- January 11, 2019: Dynamic response and hydrodynamics of polarized crowds by Zhen Li
- Deecmber 20, 2018: Neural Ordinary Differential Equations by Liu Yang
- December 20, 2018: A Proposal on Machine Learning via Dynamical Systems Liu Yang
- December 20, 2018: Identification of distributed parameter systems - A neural net based approach Liu Yang
- December 14, 2018: Spectral penalty method for the two-sided fractional differential equations with general boundary conditions by Nan Wang, Farewell
- December 14, 2018: Adversarial Uncertainty Quantification in Physics-Informed Neural Networks by Dongkun Zhang
- November 30, 2018: Indentification of physical processes via data-driven and data-assimilation methods by Xuhui Meng
- November 30, 2018: Predicting Bending Displacement of IPMC Actuators Using Parallel Non-Autoregressive Recurrent Neural Networks by Guofei Pang
- November 16, 2018: Hidden Physics Models: Machine Learning of Non-Linear Partial Differential Equations by Maziar Raissi
- November 16, 2018: Background, Clinical Features and Pathogenesis of Diabetic Retinopathy by He Li
- November 9, 2018: An introduction to multi-fidelity ensemble smoother and my ongoing projects by Qiang Zheng
- November 9, 2018: Collapse of Deep and Narrow Neural Nets by Lu Lu
- October 19, 2018: Estimation of turbulent channel flow based on wall measurements by Zhichen Liu, University of Tokyo
- October 19, 2018: Machine Learning with observers predicts complex spatiotemporal behavior by Liu Yang
- October 5, 2018: Altered blood rheology and impaired pressure-induced cutaneous vasodilation in a mouse of combined type 2 diabetes and sicle cell trait by He Li
- October 5, 2018: Analysis of prediction accuracy of classification problem based on neural networks by Lu Lu
- September 28, 2018: Spectral Fractional Diffusion: Well-posedness, Steady State, and Stochastic Solution Formulas by Mamikon Gulian
- September 28, 2019: Physical informed kriging (Phik) and Gradient-enhancing cokriging (GECK) -- paper review and summer project report by Yi-xiang Deng
- August 28, 2018: Kernel Flows: from learning kernels from data into the abyss by Guofei Pang
- August 3, 2018: Multi-level multi-fideity sparse polynomial chaos expansion based on Gaussian process regression by Dongkun Zhang
- August 3, 2018: Neural Networks 101: Implementing feedforward Neural Nets using TensorFlow by Lu Lu
- June 8, 2018: Exponential expressivity in deep neural networks through transient chaos by Yang Liu
- June 8, 2018: Deep and confident prediction for time series at Uber by Yang Liu
- June 8, 2018: GANGs: Generative adversarial network games by Yang Liu
- May 18, 2018: Doing the impossible: Why neural networks can be trained at all by Anna Lischke
- May 18, 2018: Deep Relaxation: partial differential equations for optimizing deep neural networks by Mamikon Gulian
- May 11, 2018: Deep Neural Network as Gaussina Process by Guofei Pang
- April 20, 2018: Optimal Control of momentum and scalar transfer ~ Turbulance control, shape/topology optimization, remodeling of vascular network~ by Yosuke Hasagawa
- March 30, 2018: Learning networks of stochastic differential equations by Zhiping Mao
- March 2, 2018: Approximate Bayes learning of stochastic differential equations by Ansel Blumers & Xhen Li
- February 16, 2018: 4 Years of Generative Adversarial Networks (GANs) by Lu Lu
- February 16, 2019: The Robust Manifold Defense: Adversarial Training using Generative Models by Guofei Pang
- February 2, 2018: Non-intrusive reduced order modeling of nonlinear problems using neural networks by Anna Lischke
- January 19, 2018: Brief introduction to several common neural networks by Liu Yang
- January 19, 2018: An effcient deep learning technique for the Navier-Stokes equations: Application to unsteady wake flow dynamics by Dongkun Zhang
- September 11, 2020:
- September 11, 2020: