Mission

To foster a dynamic forum for exchanging ideas, data, methods, and models related to ML techniques for fluid dynamics, turbulence, and combustion - fields crucial to the development of energy, propulsion, climate, and safety systems.

Agenda

1. A 10-day-long ML challenge (involving 1-2 person(s) per team) will be held to tackle generative modeling in fluid dynamics/turbulence with open-source data. Prize: GPU credits, and leading teams will be invited towards a joint publication.
2. Daily talks on cutting-edge trends will be given by AI/ML experts within Stanford, academic guests, and industry partners from the Greater Silicon Valley ecosystem. Topics include:

Invited Speakers (In alphabetical order)

Anima Anandkumar
Professor, Caltech
Sr. Director of AI Research, Nvidia
Neural Operators:
AI for Accelerating
Simulation and Design

Steve Brunton
Professor
University of Washington
"Machine Learning for
Scientific Discovery, with
Applications in Fluid Mechanics"

Miles Cranmer
Assistant Professor
University of Cambridge
"The Next Great
Scientific Theory is
Hiding Inside Your
Neural Network"

Tarek Echekki
Professor
North Carolina State University
"From Experiments to
Models: Challenges and
New Opportunities for
Turbulent Combustion"

Stephan Hoyer
Google Research
"Deep Learning with
Differentiable Physics for
Fluid Dynamics and
Weather Forecasting"

George Karniadakis
Professor, Brown University
"Physics-informed Neural Networks
(PINNs) and Neural Operators
for Fluid Mechanics
and Reactive Transport"

Petros Koumoutsakos
Professor, Harvard University
"Scientific Computing and Machine
Learning: There is Plenty of
Room in the Middle"

Aaron Lou
PhD Student
Stanford University
"An Introduction
to Score-Based
Diffusion Models"

Alessandro Parente
Professor
Université libre de Bruxelles
"Accelerating Reacting Flow
Simulations using Physics-aware
Data-driven Approaches"

Walter Reade
Data Scientist
Kaggle
"Open-source
Crowdscience on Kaggle"

Alex Tamkin
Stanford AI Lab
"Foundation Models
for the Sciences"

Jian-xun Wang
Assistant Professor
University of Notre Dame
"Differentiable Hybrid
Neural Modeling for
Spatiotemporal Physics"

Shashank Yellapantula
Senior Scientist, National
Renewable Energy Laboratory
"Data-Driven Reacting Flow
Model Development: Data Sampling,
Non-Linear Models and
Uncertainty Quantification"

Stay tuned for more speaker announcements…