d3s3workshop.github.io - D3S3

Description: Workshop @ NeurIPS 24

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D3S3: Data-driven and Differentiable Simulations, Surrogates, and Solvers Workshop @ NeurIPS ‘24 15th December 2024, Meeting 116-117, Vancouver Convention Center Recent advances in Machine Learning highlights promising solutions to aid simulation-based scientific discovery e.g., regulating nuclear fusion, synthesizing new molecules, designing chips. Since ML-based techniques are inherently learnable, they offer a promising solution to bridge the simulation-to-real gap and improve accuracy of simulations, an

This workshop seeks to bring experts in Machine Learning working on relevant topics (like learnable surrogates, probabilistic simulation, operator-valued models) and connect them with practitioners and researchers in interdisciplinary topics from science (e.g., physics, climate, chemistry) and engineering (e.g., wireless, graphics, manufacturing). The workshop will provide a unique platform for ML and interdisciplinary researchers to expose challenges and opportunities of integrating ML methods and simulati

We are seeking submissions in topics including, but not limited to:

Links to d3s3workshop.github.io (3)

tribhuvanesh.github.io Tribhuvanesh Orekondy
arashbehboodi.github.io Arash Behboodi
pnkraemer.github.io Nicholas (Nico) Krämer