13 citations · 41 across the 10 of their papers we have counts for
10 papers
FEABench: Evaluating Language Models on Multiphysics Reasoning Ability
Nayantara Mudur, Hao Cui, Subhashini Venugopalan +3
Building precise simulations of the real world and invoking numerical solvers to answer quantitative problems is an essential requirement in engineering and science. We present FEA…
CURIE: Evaluating LLMs On Multitask Scientific Long Context Understanding and Reasoning
Hao Cui, Zahra Shamsi, Gowoon Cheon +31
Scientific problem-solving involves synthesizing information while applying expert knowledge. We introduce CURIE, a scientific long-Context Understanding,Reasoning and Information…
Diffusion-HMC: Parameter Inference with Diffusion-model-driven Hamiltonian Monte Carlo
Nayantara Mudur, Carolina Cuesta-Lazaro, Douglas P. Finkbeiner
Diffusion generative models have excelled at diverse image generation and reconstruction tasks across fields. A less explored avenue is their application to discriminative tasks in…
Debiasing with Diffusion: Probabilistic reconstruction of Dark Matter fields from galaxies with CAMELS
Victoria Ono, Core Francisco Park, Nayantara Mudur +3
Galaxies are biased tracers of the underlying cosmic web, which is dominated by dark matter components that cannot be directly observed. Galaxy formation simulations can be used to…
Quantum Many-Body Physics Calculations with Large Language Models
Haining Pan, Nayantara Mudur, Will Taranto +5
Large language models (LLMs) have demonstrated an unprecedented ability to perform complex tasks in multiple domains, including mathematical and scientific reasoning. We demonstrat…
Cosmological Field Emulation and Parameter Inference with Diffusion Models
Nayantara Mudur, Carolina Cuesta-Lazaro, Douglas P. Finkbeiner
Cosmological simulations play a crucial role in elucidating the effect of physical parameters on the statistics of fields and on constraining parameters given information on densit…
Probabilistic reconstruction of Dark Matter fields from biased tracers using diffusion models
Core Francisco Park, Victoria Ono, Nayantara Mudur +2
Galaxies are biased tracers of the underlying cosmic web, which is dominated by dark matter components that cannot be directly observed. The relationship between dark matter densit…
Stellar Reddening Based Extinction Maps for Cosmological Applications
Nayantara Mudur, Core Francisco Park, Douglas P Finkbeiner
Cosmological surveys must correct their observations for the reddening of extragalactic objects by Galactic dust. Existing dust maps, however, have been found to have spatial corre…
Can denoising diffusion probabilistic models generate realistic astrophysical fields?
Nayantara Mudur, Douglas P. Finkbeiner
Score-based generative models have emerged as alternatives to generative adversarial networks (GANs) and normalizing flows for tasks involving learning and sampling from complex im…
Contrastive Similarity Matching for Supervised Learning
Shanshan Qin, Nayantara Mudur, Cengiz Pehlevan
We propose a novel biologically-plausible solution to the credit assignment problem motivated by observations in the ventral visual pathway and trained deep neural networks. In bot…