Bruno Régaldo-Saint Blancard — accepted AI-conference papers

2026

Walrus: A Cross-domain Foundation Model for Continuum Dynamics

Michael McCabe, Payel Mukhopadhyay, Tanya Marwah, Bruno Régaldo-Saint Blancard, François Rozet, Cristiana Diaconu +19

ICML 2026spotlight

Foundation models have transformed machine learning for language and vision, but achieving comparable impact in physical simulation remains a challenge. Data heterogeneity and unstable long-term dynamics inhibit learning from sufficiently diverse dynamics, while varying resolutions and dimensionalit…

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2024

Listening to the noise: Blind Denoising with Gibbs Diffusion

David Heurtel-Depeiges, Charles Margossian, Ruben Ohana, Bruno Régaldo-Saint Blancard

ICML 2024poster

In recent years, denoising problems have become intertwined with the development of deep generative models. In particular, diffusion models are trained like denoisers, and the distribution they model coincide with denoising priors in the Bayesian picture. However, denoising through diffusion-based p…

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2024

Multiple Physics Pretraining for Spatiotemporal Surrogate Models

Michael McCabe, Bruno Régaldo-Saint Blancard, Liam Holden Parker, Ruben Ohana, Miles Cranmer, Alberto Bietti +8

NeurIPS 2024poster

We introduce multiple physics pretraining (MPP), an autoregressive task-agnostic pretraining approach for physical surrogate modeling of spatiotemporal systems with transformers. In MPP, rather than training one model on a specific physical system, we train a backbone model to predict the dynamics o…

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2024

Simulation-Based Stacking

Yuling Yao, Bruno Régaldo-Saint Blancard, Justin Domke

AISTATS 2024poster

Simulation-based inference has been popular for amortized Bayesian computation. It is typical to have more than one posterior approximation, from different inference algorithms, different architectures, or simply the randomness of initialization and stochastic gradients. With a consistency guarantee…

Cited by 4 Source ↗PDF ↗code ↗Scholar ↗

2024

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning

Ruben Ohana, Michael McCabe, Lucas Thibaut Meyer, Rudy Morel, Fruzsina Julia Agocs, Miguel Beneitez +20

NeurIPS 2024poster

Machine learning based surrogate models offer researchers powerful tools for accelerating simulation-based workflows. However, as standard datasets in this space often cover small classes of physical behavior, it can be difficult to evaluate the efficacy of new approaches. To address this gap, we in…

Cited by 7 Source ↗PDF ↗code ↗Scholar ↗