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: Datadriven computation for invariant probability measures by Yao Li, UMass Amherst
 October 15 Recording
 October 15, 2021: Enabling Rapid Meshless Multiphysics With Hybrid DataDriven ProjectionTree ReducedOrder 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 graphbased datadriven solver for timedependent nonlinear partial differential equations by Souvik Chakraborty, IIT Delhi
 October 1, 2021: A spectral approach for timedependent PDEs using machinelearned 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: Gradientenhanced physicsinformed neural networks for forward and inverse PDE problems by Jeremy Yu, St. Mark's School of Texas
 September 10, 2021: Manifold learningbased polynomial chaos expansions for highdimensional 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 PhysicsInformedNeuralNetworks (PINNs) Applications by Dr. Ricardo Vinuesa & Hamidreza Eivazi, KTH Royal Institute of Technology
 August 27, 2021: Paper Review) PhyCRNet: Physicsinformed ConvolutionalRecurrent 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: Datadriven discovery of governing equations for fluid dynamics based on molecular simulation by Xuhui Meng, Brown University
 August 13 Recording
 August 13, 2021: PDEConstrained 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: Datadriven modeling for unsteady aerodynamics and aeroelasticity by Jiaqing Kou, Technical University of Madrid
 August 6, 2021: Explore missing flow dynamics by physicsinformed 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 ResolutionIndependent 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 twotemperature microscale heat conduction in doublelayered thin films exposed to ultrashortpulsed lasers by Aniruddha Bora, Louisiana tech University
 July 16 Recording
 July 16, 2021: Paper Review  Simulating Continuum Mechanics with MultiScale 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 multifidelity 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 HighStress 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: DataDriven Methods of Accelerating Physical Simulations and Their Applications by Youngsoo Choi, Lawrence Livermore National Laboratory
 June 4, 2021: Physicsinfused 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: SelfAdaptive PhysicallyInformed Neural Networks by Ulisses M. BragaNeto, Texas A&M University
 May 21, 2021: MorphNet: Fast & Simple ResourceConstrained 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 physicsinformed deep neural networks by N. Sukumara/Ankit Srivastava, Illinois Institute of Technology
 May 14, 2021: Physicsinformed Learning for Datadriven Discovery of Governing Laws by Hao Sun, Northeatern University
 May 7 Recording
 May 7, 2021: PhysicsConstrained ReservoirComputing for Turbulence and Chaotic Learning by Luca Magri, Imperial College London
 May 7, 2021: Hierarchical MultiInstance Learning: Theory, Some Applications, and Benefits by Tomas Pevny, Czech Technical University in Prague
 April 30 Recording
 April 30, 2021: PhysicsInformed Neural Networks for Elliptic Partial Differential Equations on 3D Manifolds by Cem Celik, Brown University
 April 30, 2021: A Caputo fractional derivativebased 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 MachineLearning Method for TimeDependent Wave Equations over Unbounded Domains by Ameya Jagtap, Brown University
 April 16 Recording
 April 16, 2021: Distributional Offline ContinuousTime Reinforcement Learning with Neural PhysicsInformed PDEs by Igor Halperin, AI AM Research, Fidelity Investments
 April 16, 2021: Learning the solution operator of parametric partial differential equations with physicsinformed DeepOnets by Somdatta Goswami, Brown University
 April 9 Recording
 April 9, 2021: Accelerating kinetic simulations of magneticconfinement fusion devices utilizing encoderdecoder neural networks to numerically solve the nonlinear FokkerPlanck 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: Hyperdifferential 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 PhysicsInformed 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 GlucoseInsulin 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 nonsmooth 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 PhysicsInformed Deep Learning by Paris Perdikaris, University of Pennsylvania, U.S.
 March 5, 2021: SIAMACM 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 multiscale PDEs with physicsinformed neural networks by Sifan Wang
 February 12, 2021: Physicsinformed 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 highdimensional 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 JongHoon Ahn
 January 22, 2021: Spectral Temporal Graph Neural Network for Multivariate Timeseries Forecasting by Zhen Zhang
 Janaury 22, 2021: Adversarial Sparse Transformer for Time Series Forecasting by Jeremy Chen
 January 15 Recording
 January 15, 2021: nnUNet: a selfconfiguring method for deep learningbased 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 NNGPguided Neural Architecture Search by Liu Yang
 December 11 Recording
 December 11, 2020: Information transfer in multitask learning by Hongyang Zhang, Assistant Professor of Computer Science at Northeastern University
 December 11, 2020: StiffPINN: PhysicsInformed 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 PointCloud Deep Learning Framework for Prediction of Fluid Flow Fields on Irregular Geometries by Ali Kashefi
 November 27, 2020: VariableOrder 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 Realtime 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 multiagent 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 highdimensional stochastic partial differential equations with physicsinformed 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 PhysicsBased and DataDriven Modeling Approach by Guanjin Wang , Mechanical engineering at University of Maryland
 October 30, 2020: Ab initio solution of the manyelectron Schrödinger equation with deep neural networks by Liu Yang
 October 23, 2020: Multiscale Deep Neural Network (MscaleDNN) Methods for Oscillatory Stokes Flows in Complex Domains by Wei Cai, Southern Methodist University
 October 23, 2020: DataDriven Multi Fidelity PhysicsInformed Constitutive MetaModeling 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 HamiltonJacobi partial differential equations via neural network architectures by Tingwei Meng
 October 9 Recording
 October 9, 2020: Solving PDE related problems using deeplearning 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: COVID19 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 PhysicsBased Modeling with Machine Learning: A Survey by Jared Willard, University of Minnesota
 September 25, 2020: Learning Solutions to Differential Equations using LSSVM 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 LevenbergMarquardt 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 LevenbergMarquardt 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: Sequencetosequence prediction of spatiotemporal systems by Zhen Zhang
 September 4, 2020: How to Deal with Imbalanced Dataset? by Yixiang Deng
 September 4, 2020: Thermodynamicsbased Artiﬁcial Neural Networks for constitutive modeling by Enrui Zhang
 August 28, 2020: Notes on Bayesian deep learning by Apostolos Psaros
 August 28, 2020: GANBERT: 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: PhysicsInformed Neural Network Framework for PDEs on 3D Surfaces by Zhiwei Fang
 July 31, 2020: OutputWeighted 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 hpVPINNs for advectiondiffusionreactions equations by Zhongqiang Zhang (Handy), WPI
 July 17, 2020: DataDriven and PhysicsConstrained 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: DataDriven Continuum Dynamics via TransportTeleport Duality by JongHoon 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 Energybased Model with Flowbased 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: Samplebased Forward and Inverse FokkerPlanck Equation Solver with Physicsinformed 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, STSenior Scientist Mathematics, Army Research Office
 May 29, 2020: DiffTaichi: Differentiable Programming for physical simulation by Leonard Gleyzer
 May 29, 2020: Datadriven Fractional Modeling for Anomalous Transport and Turbulent Flows by Mehdi Samiee
 May 22, 2020: Doubledescent 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: MetaLearning in Neural Networks: A Survey by Zongren Zou
 May 8, 2020: Transfer learning enhanced physics informed neural network for phasefield modeling of fracture by Xiaoning Zheng
 May 1, 2020: PhyGeoNet: PhysicsInformed GeometryAdaptive 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: MetaLearning 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 COVID19 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 COVID19 by Zhiping Mao
 April 10, 2020: Realvalued (medical) times series generation with recurrent conitional gans by Liu Yang
 April 3, 2020: Symplectic networks: Intrinsic structurepreserving networks for identifying Hamiltonian systems by Zhen Zhang
 April 3, 2020: Datadriven 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 PhysicsBased 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 sunglelayer 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: Dataassisted reducedorder 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 highdimensional 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 ParisSaclay, France; TU Berlin; TU Braunschweig & HITSZ
 December 13, 2019: Simulation of droplet using manybody 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 icswave 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 manybody systems with deep learning by Yang Liu
 Adjointbased 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 onepoint 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 Realtime 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 coarsegrained molecular dynamics force fields by Yixiang Deng
 August 30, 2019: Boltzmann generatorssampling equilibrium states of manybody 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: DeepPIV: 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: Datadriven 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 spatiotemporal 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 SuperResolution on SatelliteDerived 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: Superresolution reconstruction of turbulent flows with machine learning by Xiaowei Jin
 May 24, 2019: Fast and Scalable Bayesian Deep Learning by WeightPerturbation 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 multistep 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: Endtoend differentiable learning of protein structure by Yixiang Deng
 May 3, 2019: Learningbased 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 EndtoEnd and Sequential Datadriven 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: PhysicsInformed Neurak Networkd (PINNs) for high speed flows by Zhiping Mao
 April 5, 2019: Learning NoiseInvariant Representations for Robust Speech Recognition by Zhiping Mao and Xuhui Meng
 April 5, 2019: Stochastic modeling of datadriven 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 manybody 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 MoriZwanzig 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. YuHang 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: pHresponsive polymergrafted 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 twosided fractional differential equations with general boundary conditions by Nan Wang, Farewell
 December 14, 2018: Adversarial Uncertainty Quantification in PhysicsInformed Neural Networks by Dongkun Zhang
 November 30, 2018: Indentification of physical processes via datadriven and dataassimilation methods by Xuhui Meng
 November 30, 2018: Predicting Bending Displacement of IPMC Actuators Using Parallel NonAutoregressive Recurrent Neural Networks by Guofei Pang
 November 16, 2018: Hidden Physics Models: Machine Learning of NonLinear 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 multifidelity 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 pressureinduced 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: Wellposedness, Steady State, and Stochastic Solution Formulas by Mamikon Gulian
 September 28, 2019: Physical informed kriging (Phik) and Gradientenhancing cokriging (GECK)  paper review and summer project report by Yixiang Deng
 August 28, 2018: Kernel Flows: from learning kernels from data into the abyss by Guofei Pang
 August 3, 2018: Multilevel multifideity 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: Nonintrusive 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 NavierStokes equations: Application to unsteady wake flow dynamics by Dongkun Zhang
 September 11, 2020:
 September 11, 2020: