[CFP] ICLR 2024 Workshop on AI4DifferentialEquations In Science

[Boran Han]

ICLR 2024 Workshop: AI4DifferentialEquations in Science

Saturday, May 11, 2024, Vienna, Austria (Hybrid)

Workshop Website: https://ai4diffeqtnsinsci.github.io/

We are excited to invite you to join the 1^st AI4DifferentialEquations in Science Workshop taking place in Vienna, Austria during ICLR 2024. We welcome academia and industry to attend the workshop and submit papers.

Description: 

Over the past decade, the integration of Artificial Intelligence (AI) for scientific exploration has grown as a transformative force, propelling research into new realms of discovery. The AI4DifferentialEquations in Science workshop at ICLR 2024 invites participants on a dynamic journey at the interface of machine learning and computational sciences known as Scientific Machine Learning (SciML).

This workshop aims to unleash innovative approaches that harness the power of AI algorithms combined with computational mathematics to advance scientific discovery and problem solving. This enables us to push the boundaries of scientific computing beyond its traditional limits. Our goal is to delve into the latest AI advancements, particularly those that significantly enhance the efficiency of solving ordinary and partial differential equations (PDEs). These methods result in significant performance gains, which allow for solutions at high resolution that were previously unfeasible or required large amounts of computation. The AI4DifferentialEquations in Science workshop aims to unlock the full potential of data-driven approaches in advancing scientific frontiers in earth sciences, climate and computational fluid dynamics to name a few.

The AI4DifferentialEquations in Science workshop will be an interactive event for researchers and practitioners at various stages of their careers with diverse backgrounds to come together and share their perspectives and experiences on leveraging AI techniques for their particular science problems. We aim to better bridge the gaps between the different fields of numerical analysis, machine learning and the sciences by sharing insights, methodologies, and challenges in harnessing AI’s power for the greater good of scientific exploration.

Topics:

Key topics include but are not limited to:

• Exploration of novel applications of deep learning techniques in scientific simulations of partial or ordinary differential equations.
• Forward and inverse problems in PDEs to equation discovery, design optimization, and beyond, to witness the diverse applications of AI in scientific pursuits.
• Explainability and interpretability of AI models in scientific contexts.

Important Dates/Links:

• Submission Deadline: February 3^rd, 2024
• Submission Link: https://openreview.net/group?id=ICLR.cc/2024/Workshop/AI4DiffEqtnsInSci
• Acceptance Notification: March 3^rd, 2023

Invited Speakers:

• Johannes Brandstetter (Assistant Professor at Johannes Kepler University and Guest Researcher at Microsoft Research Amsterdam)
• Steve Brunton (James B. Morrison Endowed Career Development Professor in Mechanical Engineering and Adjunct Professor of Applied Mathematics at the University of Washington)
• Youngsoo Choi (Computational Math Scientist in the Center of Applied Scientific Computing (CASC) at the Lawrence Livermore National Laboratory (LLNL))
• Meire Fortunato (Senior Research Scientist at DeepMind)
• Paula Harder (Ph.D. student in Computer Science at the Fraunhofer Institute for Industrial Mathematics (ITWM) and the Scientific Computing Group (SciComp) at the University of Kaiserslautern)
• Jakob Macke (Professor for “Machine Learning in Science” at the University of Tübingen)
• Chris Rackauckas (Research Affiliate and Co-PI of the Julia Lab at the Massachusetts Institute of Technology)
• Alex Townsend (Associate Professor of Mathematics at Cornell University)

Panelists:

• Kevin Carlberg (Director of AI Research Science at Meta Reality Labs Research and an Affiliate Associate Professor of Applied Mathematics and Mechanical Engineering at the University of Washington)
• Marta D’Elia (Principal Research and Computational Scientist at Pasteur Labs and an Adjunct Professor at the Institute of Computational and Mathematical Engineering at Stanford University)
• Shirley Ho (Research Professor of Physics and Affiliated Faculty at the Center for Data Science at New York University and leader of the Cosmology X Data Science group at theSimons Foundation)
• Max Welling (Research chair in Machine Learning at the University of Amsterdam and Distinguished Scientist at Microsoft)

Best regards ,

1^st AI4DifferentialEquations In Science Workshop Organizing Committee

[Boran Han]

We're excited to announce that the paper submission deadline for our ICLR 2024 Workshop has been extended to February 10th. Don't miss this opportunity to contribute to the vibrant discussions on AI4DifferentialEquations in Science!