2 citations · 2 across the 5 of their papers we have counts for
5 papers
Foundation Models for Discovery and Exploration in Chemical Space
Alexius Wadell, Anoushka Bhutani, Victor Azumah +26
Accurate prediction of atomistic, thermodynamic, and kinetic properties from molecular structures underpins materials innovation. Existing computational and experimental approaches…
The False Promise of Zero-Shot Super-Resolution in Machine-Learned Operators
Mansi Sakarvadia, Kareem Hegazy, Amin Totounferoush +4
A core challenge in scientific machine learning, and scientific computing more generally, is modeling continuous phenomena which (in practice) are represented discretely. Machine-l…
Residuals Are Not Enough: Limits of Physics-Informed Pre-Training for Scientific Foundation Models
Amin Totounferoush, Serge Kotchourko, Michael W. Mahoney +1
Scientific foundation models (SciFMs) aim to learn generalizable representations of physical systems governed by partial differential equations (PDEs), enabling transfer across tas…
Recency Biased Causal Attention for Time-series Forecasting
Kareem Hegazy, Michael W. Mahoney, N. Benjamin Erichson
Recency bias is a useful inductive prior for sequential modeling: it emphasizes nearby observations and can still allow longer-range dependencies. Standard Transformer attention la…
Neural equilibria for long-term prediction of nonlinear conservation laws
J. Antonio Lara Benitez, Kareem Hegazy, Junyi Guo +3
Nonlinear conservation laws govern a broad class of important physical systems in science and industry and are central to scientific machine learning (SciML). Large general-purpose…