Machine Learning + X Seminars 2023

October 27 Recording
October 27, 2023: Catalyst Property Prediction with CatBERTa: Unveiling Feature Exploration Strategies through Large Language Models by Janghoon Ock, Carnegie Mellon University
October 27, 2023: Language Models by Jan Drgona, Pacific Northwest National Laboratory
October 20 Recording
October 20, 2023: DeepONets for Varying Geometries and Time-dependent Loads by Jimmy He, Ansys Inc.
October 20, 2023: Using physics to enhance machine learning models: The “Toy Model” Concept by Indranil Brahma, Bucknell University
October 13 Recording
October 13, 2023: Nonlinear Mode Decomposition via Physics-Assimilated Neural Network by Bo Zhang, University of Notre Dame
October 13, 2023: A gradient-enhanced physics-informed neural network (gPINN) scheme for the coupled non-Fickian/non-Fourierian diffusion-thermoelasticity analysis by Katayoun Eshkofti, Ferdowsi University
October 6 Recording
October 6, 2023: Regularization by architecture: Deep learning for PDE based inverse problems by Peter Maass, Derick Nganyu Tanyu, Janek Gödeke, University of Bremen
October 6, 2023: Analysis and Application of PINNs for Two-Phase Interface Problems by Xiaozhe Hu, Tufts University
September 29 Recording
September 29, 2023: Leveraging Temporal Dynamics in Training Spiking Neural Networks by Jason Eshraghian, University of California, Santa Cruz
September 29, 2023: Real-Time Decision Making in Microscopy and Spectroscopy by Mitra Taheri, Johns Hopkins University
September 22 Recording
September 22, 2023: Paper review: Deep learning analysis of blood flow sounds to detect arteriovenous fistula stenosis by Nikhil Kadivar, Additi Pandey, Brown University
September 22, 2023: On the training and generalization of deep operator networks by Yeonjong Shin, North Carolina State University
September 15 Recording
September 15, 2023: Data Driven, multi-scale dynamics for complex social systems by Nick Gabriel, George Washington University
September 15, 2023: Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration by Zhongyi Jiang, University of Delaware
September 8 Recording
September 8, 2023: From bi-parametric operator preconditioning to operator learning by Paul Escapil, Data Observatory
September 8, 2023: A hybrid Decoder-DeepONet operator regression framework for unaligned observation data by Weipeng Li, Shanghai Jiaotong University
September 1 Recording
September 1, 2023: Out-of-distributional risk bounds for neural operators with applications to the Helmholtz equation by Jose Antonio Lara Benitez, Rice University
August 25 Recording
August 25, 2023: Physics-Driven Synthetic Data Learning for Biomedical Magnetic Resonance by Xiaobo Qu, Xiamen University
August 25, 2023: Literature Review: ChatGPT in Science by Chenxi Wu, Brown University
August 18 Recording
August 18, 2023: Physics informed neural networks for surrogate modeling of accidental scenarios in nuclear power plants by Federico Antonello, European Space Agency
August 18, 2023: GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward non-intrusive Meta-learning of parametric PDEs by Yanlai Chen, University of Massachusetts Dartmouth
August 11 Recording
August 11, 2023: Physics-informed Machine Learning in Gaussian Process Regression by Jinhyeun Kim, Georgia Institute of Technology
August 11, 2023: Physics-informed neural networks for predicting gas flow dynamics and unknown parameters in diesel engines by Kamaljyoti Nath, Brown University
August 11, 2023: Real-Time Prediction of Gas Flow Dynamics in Diesel Engines using a Deep Neural Operator Framework by Varun Kumar, Brown University
August 4 Recording
August 4, 2023: Physics-informed neural networks for non-smooth PDE-constrained optimization problems by Yongcun Song, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany
August 4, 2023: Corrector Operator to Enhance Accuracy and Reliability of Neural Operator Surrogates of Nonlinear Variational Boundary-Value Problems by Prashant K. Jha, The University of Texas at Austin
July 28 Recording
July 28, 2023: Neural Inverse Operators for Solving PDE Inverse Problems by Roberto Molinaro, ETH Zurich
July 28, 2023: Gradient-Annihilated PINNs for Solving Riemann Problems: Application to Relativistic Hydrodynamics by Antonio Ferrer-Sánchez and José Antonio Font, University of Valencia (Spain)
July 21 Recording
July 21, 2023: GAS: A Gaussian Mixture Distribution-based Adaptive Sampling Method for PINNs by Xiliang Lu, Wuhan University
July 21, 2023: Towards Foundational Dynamics: From Invariant Neural ODEs to PINNs for Stroke to Mechanics Neural Networks by Stratis Gavves, University of Amsterdam
July 14 Recording
July 14, 2023: Residual-based attention and connection to information bottleneck theory in PINNs by Sokratis J. Anagnostopoulos, Juan Diego Toscano, Brown University
July 14, 2023: An enrichment approach for enhancing the expressivity of neural operators with applications to seismology by Ehsan Haghighat (Carbon), Umair bin Waheed (King Fahd University of Petroleum and Minerals (KFUPM))
July 7 Recording
July 7, 2023: Deep Data Driven Neural Networks for Learning Dynamics Of COVID-19 Epidemic Models by Thomas K. Torku, Middle Tennessee State University
July 7, 2023: ODEs learn to walk: ODE-Net based data-driven modeling for crowd dynamics by Chen Cheng, Shanghai Jiao Tong University
June 30 Recording
June 30, 2023: (paper study) NAS-PINN: Neural architecture search-guided physics-informed neural network for solving PDEs by Zongren Zou, Brown University
June 30, 2023: Universal Physics-Informed Neural Networks: Symbolic Differential Operator Discovery in Biological Sciences by Lena Podina, University of Waterloo
June 23 Recording
June 23, 2023: Data-Driven Stabilization Schemes for Singularly Perturbed Partial Differential Equations by Sangeeta Yadav, Indian Institute of Science, Bangalore, India
June 23, 2023: Parsimonious Physics-informed Random Projection Neural Networks (RPNN) for solving Forward and Inverse Problems by Gianluca Fabiani, Scuola Superiore Meridionale (SSM), Naples, Italy
June 16 Recording
June 16, 2023: Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations by Wenqian Chen, Pacific Northwest National Laboratory
June 16, 2023: Exploring the geometry transferability properties of the HINTS by Adar Kahana, Brown University
June 2 Recording
June 2, 2023: A multifidelity deep operator network approach to closure for multiscale systems by Shady Ahmed, Pacific Northwest National Laboratory (PNNL)
June 2, 2023: Efficient Bayesian inference using physics-informed invertible neural networks for inverse problems by Hao Wu, Tongji University, China
May 26 Recording
May 26, 2023: Fixing Physics-informed Neural Networks (PINNs) by Shams Basir, Bristol Myers Squibb
May 26, 2023: ChatGPT for Programming Numerical Methods by Ali Kashefi, Stanford University
May 19 Recording
May 19, 2023: The trial and errors of physically informing neurons by Tariq Alkhalifa, KAUST
May 19, 2023: A new SAV-based minimization algorithm for discrete gradient system by Xinyu Liu, Purdue University
May 12 Recording
May 12, 2023: Rockafellian functions in optimization and learning by Johannes Royset, Naval Postgraduate School
May 12, 2023: Theoretical Analysis of Boundary Penalties for NN Based PDE Solvers by Johannes Muller, Max Planck Institute for Mathematics in the Sciences
May 12, 2023: Energy Natural Gradient Methods for PINNs by Marius Zeinhofer, Simula Research Laboratory, Oslo, Norway
May 5 Recording
May 5, 2023: 3D Design Using Generative Adversarial Networks and Physics-Based Validation by Varun Kumar, Brown University
May 5, 2023: Ensemble Learning for Physics Informed Neural Networks: A Gradient Boosting Approach by Zhiwei Fang, Amazon
April 28 Recording
April 28, 2023: Applications of machine learning in improving the assessment of myocardial remodeling in cardiac diseases by Reza Avazmohammadi, Texas A&M University
April 28, 2023: Pseudo-Hamiltonian neural networks for learning partial differential equations by Sølve Eidnes, SINTEF Digital, Oslo, Norway
April 21 Recording
April 23, 2023: From Physics-Informed to Physics-Free Control of Complex Systems via Machine Learning by Constantinos Siettos, University of Naples Federico II
April 23, 2023: DeepONet Provably Obviates Computation in Control of Unstable PDEs by Miroslav Krstic, University of California San Diego (UCSD)
April 23, 2023: Leveraging multi-time Hamilton-Jacobi PDEs for certain scientific machine learning problems by Jerome Darbon, Brown University
April 12 Recording
April 12, 2023: iPINNs: Incremental learning for Physics-informed neural networks by Miguel Bessa, Aleksandr Dekhovich, Delft University of Technology
April 12, 2023: Machine Learning Based Improved NDE Methods for Defect Diagnostics by Subrata Mukherjee, Michigan State University (MSU)
April 7 Recording
April 7, 2023: Solving free-surface problems for non-shallow water using boundary and initial conditions-free physics-informed neural network (bif-PINN) by Yinghao Huang, BirenTech, China
April 7, 2023: Physics-informed neural networks for the Euler equations and ice dynamics by Ching-Yao Lai, Princeton University
March 31 Recording
March 31, 2023: LNO: Laplace Neural Operator for Solving Differential Equations by Qianying Cao, Brown University
March 31, 2023: Learning dynamics of complex systems from partial observations by George Stepaniants, Massachusetts Institute of Technology
March 24 Recording
March 24, 2023: DeepParticle: learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method by Zhiwen Zhang, the University of Hong Kong
March 24, 2023: Physics-assisted machine-learning models in complex fluid systems by Cristina Martin-Linares, Johns Hopkins University
March 17 Recording
March 17, 2023: A radical approach to deep learning that is also biologically plausible by Timoleon Moraitis, Huawei, Switzerland
March 17, 2023: Data-Driven Model Discovery for Non-equilbrium Processes via Stochastic Thermomechanics by Shenglin Huang, University of Pennsylvania
March 10 Recording
March 10, 2023: Convolutional and Physics-Based Neural Networks for Solid/Structural Mechanics Problems by Vikas Srivastava, Brown University
March 10, 2023: Multifidelity continual learning by Amanda Howard, Pacific Northwest National Laboratory
March 3 Recording
March 3, 2023: Data-driven and physics-informed DeepONets for the solution of Poisson’s equation with parametric source term by Seid Koric, University of Illinois Urbana-Champaign
March 3, 2023: The Theory of Functional Connections: Current Status by Daniele Mortari, Texas A&M University
February 23 Recording
February 23, 2023: Convergence analysis of the multi-scale deep neural network (MscaleDNN) by Bo Wang, Huan Normal University
February 23, 2023: Loss Landscape Engineering via Data Regulation on PINNs by Paula Chen, Brown University
February 17 Recording
February 17, 2023: Tensor Neural Network and Its Applications by Hehu Xie, Chinese Academy of Sciences
February 17, 2023: Closure of Non-Equilibrium Dynamics Using Deep Learning and application in polymer dynamics by Xiaoli Chen, National University of Singapore
February 10 Recording
February 10, 2023: A Comprehensive Approach to Process Coupling in Atmospheric Models: Theory, Software, and Applications by Ubbiali Stefano, ETH Zurich
February 10, 2023: Kolmogorov n-width, from low-rank registration based auto-encoders to Lagrangian physics informed neural networks by Rambod Mojgani, Rice University
February 3 Recording
February 3, 2023: Condensation in deep learning by Zhiqin Xu, Shanghai Jiao Tong University
February 23, 2023: Separable PINN: Mitigating the Curse of Dimensionality in Physics-Informed Neural Networks by Eunbyung Park, Sungkyunkwan University
January 27 Recording
Janaury 27, 2023: Data-driven discovery of dimensionless numbers and governing laws from scarce measurements by Zhengtao Gan, University of Texas at El Paso
Janaury 27, 2023: Learning Nonlocal Constitutive Models with Neural Operators by Jiequn Han, Flatiron Institute, Simons Foundation
January 20 Recording
January 20, 2023: Scientific machine learning for modeling near-wall mass transport and boundary layers by Amirhossein Arzani, University of Utah
January 20, 2023: Physics-Informed Neural Networks, Extreme Learning Machine, Theory of Functional Connections. A holistic picture by Mario De Florio, Brown University
January 13 Recording
January 13, 2023: Kalman-Bucy-Informed Neural Networks for System Identification by Tobias Heinrich Nagel and Marco Huber, Fraunhofer Institute for Manufacturing Engineering and Automation IPA
January 13, 2023: Hybrid physics-data analysis and modeling: an inductive bias perspective by Omer San, Oklahoma State University
January 6 Recording
January 6, 2023: AI for accelerated catalyst discovery Alexandre Duval, CentraleSupelec & Inria, and Yoshua by Bengio, Université de Montreal
January 6, 2023: Continual learning: A introduction by Zongren Zou, Brown University