40 citations · 41 across the 10 of their papers we have counts for
11 papers
GPU-Enabled Large-Scale Optimization Using Randomized Linear Algebra
Pratik Rathore, Zachary Frangella, Parth Nobel +2
This paper introduces rlaopt, a PyTorch-based package for large-scale optimization and scientific computing using randomized numerical linear algebra (RandNLA). Despite substantial…
ShapeBench: A Scalable Benchmark and Diagnostic Suite for Standardized Evaluation in Aerodynamic Shape Optimization
Shaghayegh Fazliani, Krissh Chawla, Jack Guo +3
Rapid progress in aerodynamic shape optimization (ASO) has outpaced currently-available standardized evaluation frameworks. Fair comparison requires a unified benchmark spanning di…
SubsurfaceGen: Procedural Generation of Field-Scale Earth Models and Seismic Data
Joseph Stitt, Pratik Rathore, Madeleine Udell +1
Full waveform inversion (FWI) is the gold standard for subsurface imaging, with applications from carbon sequestration to energy and mineral exploration to earthquake hazard assess…
PDE-SHARP: PDE Solver Hybrids through Analysis and Refinement Passes
Shaghayegh Fazliani, Madeleine Udell
Current LLM-driven approaches using test-time computing to generate PDE solvers execute a large number of solver samples to identify high-accuracy solvers. These paradigms are espe…
Turbocharging Gaussian Process Inference with Approximate Sketch-and-Project
Pratik Rathore, Zachary Frangella, Sachin Garg +3
Gaussian processes (GPs) play an essential role in biostatistics, scientific machine learning, and Bayesian optimization for their ability to provide probabilistic predictions and…
Enhancing Physics-Informed Neural Networks Through Feature Engineering
Shaghayegh Fazliani, Zachary Frangella, Madeleine Udell
Physics-Informed Neural Networks (PINNs) seek to solve partial differential equations (PDEs) with deep learning. Mainstream approaches that deploy fully-connected multi-layer deep…
SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning
Jingruo Sun, Zachary Frangella, Madeleine Udell
Regularized empirical risk minimization (rERM) has become important in data-intensive fields such as genomics and advertising, with stochastic gradient methods typically used to so…
Have ASkotch: A Neat Solution for Large-scale Kernel Ridge Regression
Pratik Rathore, Zachary Frangella, Jiaming Yang +2
Kernel ridge regression (KRR) is a fundamental computational tool, appearing in problems that range from computational chemistry to health analytics, with a particular interest due…
Challenges in Training PINNs: A Loss Landscape Perspective
Pratik Rathore, Weimu Lei, Zachary Frangella +2
This paper explores challenges in training Physics-Informed Neural Networks (PINNs), emphasizing the role of the loss landscape in the training process. We examine difficulties in…
GeNIOS: an (almost) second-order operator-splitting solver for large-scale convex optimization
Theo Diamandis, Zachary Frangella, Shipu Zhao +2
We introduce the GEneralized Newton Inexact Operator Splitting solver (GeNIOS) for large-scale convex optimization. GeNIOS speeds up ADMM by approximately solving approximate subpr…
PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates
Zachary Frangella, Pratik Rathore, Shipu Zhao +1
This paper introduces PROMISE (econditioned Stochastic ptimization ethods by ncorporating calable Curvature $\textbf{E}…