21 citations · 33 across the 10 of their papers we have counts for
10 papers
Explain Like I'm Five: Using LLMs to Improve PDE Surrogate Models with Text
Cooper Lorsung, Amir Barati Farimani
Solving Partial Differential Equations (PDEs) is ubiquitous in science and engineering. Computational complexity and difficulty in writing numerical solvers has motivated the devel…
Strategies for Pretraining Neural Operators
Anthony Zhou, Cooper Lorsung, AmirPouya Hemmasian +1
Pretraining for partial differential equation (PDE) modeling has recently shown promise in scaling neural operators across datasets to improve generalizability and performance. Des…
PICL: Physics Informed Contrastive Learning for Partial Differential Equations
Cooper Lorsung, Amir Barati Farimani
Neural operators have recently grown in popularity as Partial Differential Equation (PDE) surrogate models. Learning solution functionals, rather than functions, has proven to be a…
Physics Informed Token Transformer for Solving Partial Differential Equations
Cooper Lorsung, Zijie Li, Amir Barati Farimani
Solving Partial Differential Equations (PDEs) is the core of many fields of science and engineering. While classical approaches are often prohibitively slow, machine learning model…
Neural Network Predicts Ion Concentration Profiles under Nanoconfinement
Zhonglin Cao, Yuyang Wang, Cooper Lorsung +1
Modeling the ion concentration profile in nanochannel plays an important role in understanding the electrical double layer and electroosmotic flow. Due to the non-negligible surfac…
PyQMC: an all-Python real-space quantum Monte Carlo module in PySCF
William A. Wheeler, Shivesh Pathak, Kevin Kleiner +11
We describe a new open-source Python-based package for high accuracy correlated electron calculations using quantum Monte Carlo (QMC) in real space: PyQMC. PyQMC implements modern…
MeshDQN: A Deep Reinforcement Learning Framework for Improving Meshes in Computational Fluid Dynamics
Cooper Lorsung, Amir Barati Farimani
Meshing is a critical, but user-intensive process necessary for stable and accurate simulations in computational fluid dynamics (CFD). Mesh generation is often a bottleneck in CFD…
On Thermodynamic Interpretation of Copula Entropy
Jian Ma
Copula Entropy (CE) is a recently introduced concept for measuring correlation/dependence in information theory. In this paper, the theory of CE is introduced and the thermodynamic…
Understanding Uncertainty in Bayesian Deep Learning
Cooper Lorsung
Neural Linear Models (NLM) are deep Bayesian models that produce predictive uncertainty by learning features from the data and then performing Bayesian linear regression over these…
Uncertainty-Aware (UNA) Bases for Deep Bayesian Regression Using Multi-Headed Auxiliary Networks
Sujay Thakur, Cooper Lorsung, Yaniv Yacoby +2
Neural Linear Models (NLM) are deep Bayesian models that produce predictive uncertainties by learning features from the data and then performing Bayesian linear regression over the…