Machine Learning + X seminars are held weekly.
Selected presentations from past seminars are posted below.
- 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: