Scientific Machine Learning, or SciML for short, is a new and exciting area that combines science with machine learning. Recently, it's led to some interesting breakthroughs. However, using machine learning for scientific problems isn't easy. One big issue is that the models we use in machine learning are often complex and hard to understand. SciML tries to solve this by breaking down these complicated models into smaller, easier-to-understand pieces. This way, we can use well-known scientific methods to study them better. Plus, by adding in what we already know about the science behind the problems, we can make the models' predictions more reliable and easier to explain. We meet every two weeks to talk about the latest methods at the intersection of machine learning and dynamical systems. This seminar aims to explore challenges and solutions to make machine learning models more trustworthy and easy to understand for scientific applications. The seminar is hosted by

Please, sign up for our SciML Google Group in order to receive updates and announcements!

Our meetings are held on Zoom:

Spring semester 2023: 1pm (PST) / 4pm (EST)

Fall semester 2022: 9 am (PST) / 12pm (EST) / 6pm (CET)

  • Friday, September 9, 2022: Lu Lu (University of Pennsylvania)
    • Title: Learning operators using deep neural networks for multiphysics, multiscale, & multifidelity problems (video)
  • Friday, September 23, 2022: Omer San (Oklahoma State University)
    • Title: Hybrid physics-data modeling in fluid dynamics (video)
  • Friday, October 7, 2022: Jan Drgona (PNNL)
    • Title: Differentiable Programming for Modeling and Control of Dynamical Systems (video)
  • Friday, October 21, 2022: Themistoklis Sapsis (MIT)
    • Title: Likelihood-weighted active learning with application to Bayesian optimization, uncertainty quantification, and decision making in high dimensions
  • Friday, November 4, 2022: Youngsoo Choi (LLNL)
    • Title: Interpretable and physics-constrained data-driven methods for physical simulations
  • Friday, November 18, 2022: Lars Ruthotto (Emory University)
    • Title: Neural Network Approaches for High-Dimensional Optimal Control
  • Friday, December 16, 2022: Shams Basir (University of Pittsburgh)
    • Title: Integrating Noisy Data, High Fidelity Data and Governing Laws to Extract Physical Solutions using Artificial Neural Networks

Spring semester 2022: 9 am (PST) / 12pm (EST) / 6pm (CET)

  • Tuesday, January 25, 2022: Weinan E (Peking University)
    • Title: A Mathematical Perspective of Machine Learning (video)
  • Friday, February 4, 2022: Alex Townsend (Cornell University)
    • Title: Learning Green’s functions associated with elliptic PDEs (video)
  • Friday, February 18, 2022: Cyril Zhang (Microsoft Research, NYC)
    • Title: Understanding Neural Net Training Dynamics in Tractable Slices (video)
  • Friday, March 4, 2022: Bao Wang (University of Utah)
    • Title: How Differential Equations and Random Graph Insights BenefitDeep Learning
  • Friday, March 18, 2022: Houman Owhadi (Caltech)
    • Title: Solving/learning nonlinear PDEs and completing computational graphs with GPs (video)
  • Friday, April 1, 2022: Rose Yu (UC San Diego)
    • Title: Exploiting Symmetry in Deep Dynamics Models For Improved Generalization (video)
  • Friday, April 15, 2022: William Gilpin (UT Austin)
    • Title: Comparing statistical forecasting models across a space of chaotic systems (video)
  • Friday, April 29, 2022: Lorenzo Livi (University of Manitoba)
    • Title: Training and Dynamics of Recurrent Neural Networks (video)
  • Friday, May 27, 2022: Mohammad Farazmand (NC State University)
    • Title: Data-driven prediction of dynamical systems from partial observations (video)

Previous Co-Organizers