Machine Learning + X Seminars 2023

December 15, 2023: Multiagent Trajectory Planning by Katia Estabridis, Naval Air Weapons Center Weapons Division
December 08 Recording
December 08, 2023: Utilising Machine Learning and Phenolic Compound Analysis to Enhance Quality Control and Protect the Provenance of New Zealand Pinot Noir Wines by Jingxian An, University of Auckland, New Zealand
December 08, 2023: Physics Informed Machine Learning through Symbolic Regression by George Bollas, University of Connecticut
December 01 Recording
December 01, 2023: Zero Coordinate Shift: Whetted Automatic Differentiation for Physics-informed Operator Learning by Kuangdai Leng, Scientific Computing Department, STFC, UK
December 01, 2023: DeepMartNet – A Martingale based Deep Neural Network Algorithm for Eigenvalue/BVP Problems of PDEs and Optimal Stochastic Controls by Wei Cai, Southern Methodist University
November 17 Recording
November 17, 2023: Failure-informed adaptive sampling for PINNs by Zhiwei Gao, Southeast University, China
November 17, 2023: GRINN: A Physics-Informed Neural Network for solving hydrodynamic systems in the presence of self-gravity by Sayantan Auddy, NASA Jet Propulsion Laboratory (JPL), California Institute of Technology
November 10 Recording
November 10, 2023: In-Context Operator Networks (ICON): Towards Large Scientific Learning Models by Liu Yang, University of California, Los Angeles
November 03 Recording
November 03, 2023: Solution multiplicity and effects of data and eddy viscosity on Navier-Stokes solutions inferred by physics-informed neural networks by Zhicheng Wang, Dalian University of Technology
November 03, 2023: Paper review – A Survey on Physics Informed Reinforcement Learning: Review and Open Problems by Varun Kumar, Brown University
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