17 citations · 25 across the 12 of their papers we have counts for
5 papers · 1 filter
Towards Infinitely Long Neural Simulations: Self-Refining Neural Surrogate Models for Dynamical Systems
Qi Liu, Laure Zanna, Joan Bruna
Recent advances in autoregressive neural surrogate models have enabled orders-of-magnitude speedups in simulating dynamical systems. However, autoregressive models are generally pr…
Accelerating scientific discovery with the common task framework
J. Nathan Kutz, Peter Battaglia, Michael Brenner +12
Machine learning (ML) and artificial intelligence (AI) algorithms are transforming and empowering the characterization and control of dynamic systems in the engineering, physical,…
Fourier analysis of the physics of transfer learning for data-driven subgrid-scale models of ocean turbulence
Moein Darman, Pedram Hassanzadeh, Laure Zanna +1
Transfer learning (TL) is a powerful tool for enhancing the performance of neural networks (NNs) in applications such as weather and climate prediction and turbulence modeling. TL…
Thermalizer: Stable autoregressive neural emulation of spatiotemporal chaos
Chris Pedersen, Laure Zanna, Joan Bruna
Autoregressive surrogate models (or \textit{emulators}) of spatiotemporal systems provide an avenue for fast, approximate predictions, with broad applications across science and en…
A Monte Carlo Framework for Calibrated Uncertainty Estimation in Sequence Prediction
Qidong Yang, Weicheng Zhu, Joseph Keslin +3
Probabilistic prediction of sequences from images and other high-dimensional data is a key challenge, particularly in risk-sensitive applications. In these settings, it is often de…