6 citations · 6 across the 6 of their papers we have counts for
6 papers
A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications
Shridhar Vashishtha, Krishna Prasath Logakannan, Jacob Hochhalter +2
Digital twins are developed to model the behavior of a specific physical asset (or twin), and they can consist of high-fidelity physics-based models or surrogates. A highly accurat…
SIFBench: An Extensive Benchmark for Fatigue Analysis
Tushar Gautam, Robert M. Kirby, Jacob Hochhalter +1
Fatigue-induced crack growth is a leading cause of structural failure across critical industries such as aerospace, civil engineering, automotive, and energy. Accurate prediction o…
Mask-Based Deep Symbolic Regression
Zachary Bastiani, Robert M. Kirby, Jacob Hochhalter +1
Diffusion has emerged as a powerful framework for generative modeling, achieving remarkable success in applications such as image and audio synthesis. Enlightened by this progress,…
Pseudo-Physics-Informed Neural Operators: Enhancing Operator Learning from Limited Data
Keyan Chen, Yile Li, Da Long +4
Neural operators have shown great potential in surrogate modeling. However, training a well-performing neural operator typically requires a substantial amount of data, which can po…
Complexity-Aware Deep Symbolic Regression with Robust Risk-Seeking Policy Gradients
Zachary Bastiani, Robert M. Kirby, Jacob Hochhalter +1
We propose a novel deep symbolic regression approach to enhance the robustness and interpretability of data-driven mathematical expression discovery. Our work is aligned with the p…
Genetic Programming Based Symbolic Regression for Analytical Solutions to Differential Equations
Hongsup Oh, Roman Amici, Geoffrey Bomarito +3
In this paper, we present a machine learning method for the discovery of analytic solutions to differential equations. The method utilizes an inherently interpretable algorithm, ge…