167 citations · 1.3k across the 78 of their papers we have counts for
12 papers · 1 filter
Out-of-Distribution Detection in Molecular Complexes via Diffusion Models for Irregular Graphs
David Graber, Victor Armegioiu, Rebecca Buller +1
Predictive machine learning models generally excel on in-distribution data, but their performance degrades on out-of-distribution (OOD) inputs. Reliable deployment therefore requir…
Fluid Intelligence: A Forward Look on AI Foundation Models in Computational Fluid Dynamics
Neil Ashton, Johannes Brandstetter, Siddhartha Mishra
Driven by the advancement of GPUs and AI, the field of Computational Fluid Dynamics (CFD) is undergoing significant transformations. This paper bridges the gap between the machine…
Universality of physical neural networks with multivariate nonlinearity
Benjamin Savinson, David J. Norris, Siddhartha Mishra +1
The enormous energy demand of artificial intelligence is driving the development of alternative hardware for deep learning. Physical neural networks try to exploit physical systems…
Towards a Certificate of Trust: Task-Aware OOD Detection for Scientific AI
Bogdan Raonić, Siddhartha Mishra, Samuel Lanthaler
Data-driven models are increasingly adopted in critical scientific fields like weather forecasting and fluid dynamics. These methods can fail on out-of-distribution (OOD) data, but…
HyPINO: Multi-Physics Neural Operators via HyperPINNs and the Method of Manufactured Solutions
Rafael Bischof, Michal Piovarči, Michael A. Kraus +2
We present HyPINO, a multi-physics neural operator designed for zero-shot generalization across a broad class of PDEs without requiring task-specific fine-tuning. Our approach comb…
Functional Neural Wavefunction Optimization
Victor Armegioiu, Juan Carrasquilla, Siddhartha Mishra +4
We propose a framework for the design and analysis of optimization algorithms in variational quantum Monte Carlo, drawing on geometric insights into the corresponding function spac…