Christopher J. Earls

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 21

Top Topics

Monte Carlo Method

Partial Differential Equations

Heterogeneous Computing

Top Venues

IEEE Trans. Geosci. Remote. Sens.

Neural Networks

Publications

Robert Stephany, Christopher J. Earls
PDE-LEARN: Using deep learning to discover partial differential equations from noisy, limited data. Neural Networks 174 (2024)
Toni J. B. Liu, Nicolas Boullé, Raphaël Sarfati, Christopher J. Earls
LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling law. CoRR (2024)
Robert Stephany, Christopher J. Earls
Weak-PDE-LEARN: A weak form based approach to discovering PDEs from noisy, limited data. J. Comput. Phys. 506 (2024)
Robert Stephany, Christopher J. Earls
Weak-PDE-LEARN: A Weak Form Based Approach to Discovering PDEs From Noisy, Limited Data. CoRR (2023)
Robert Stephany, Christopher J. Earls
PDE-READ: Human-readable partial differential equation discovery using deep learning. Neural Networks 154 (2022)
Harshwardhan Praveen, Nicolas Boullé, Christopher J. Earls
Principled interpolation of Green's functions learned from data. CoRR (2022)
Robert Stephany, Christopher J. Earls
PDE-LEARN: Using Deep Learning to Discover Partial Differential Equations from Noisy, Limited Data. CoRR (2022)
Christophe Bonneville, Christopher J. Earls
Bayesian deep learning for partial differential equation parameter discovery with sparse and noisy data. J. Comput. Phys. X 16 (2022)
Wensi Wu, Christopher J. Earls
A new engineering theory describing oblique free surface impact by flexible plates. CoRR (2021)
John Taylor Chavis III, Amy L. Cochran, Christopher J. Earls
CU-MSDSp: A flexible parallelized Reversible jump Markov chain Monte Carlo method. SoftwareX 14 (2021)
Robert Stephany, Christopher J. Earls
PDE-READ: Human-readable Partial Differential Equation Discovery using Deep Learning. CoRR (2021)
Hilarie Sit, Christopher J. Earls
Deep learning for classifying and characterizing atmospheric ducting within the maritime setting. Comput. Geosci. 157 (2021)
Christophe Bonneville, Christopher J. Earls
Bayesian Deep Learning for Partial Differential Equation Parameter Discovery with Sparse and Noisy Data. CoRR (2021)
Nicolas Boullé, Christopher J. Earls, Alex Townsend
Data-driven discovery of physical laws with human-understandable deep learning. CoRR (2021)
Christophe Bonneville, Maxwell Jenquin, Juan Londono, Alex Kelly, Jeffrey Cipolla, Christopher J. Earls
Gaussian processes for shock test emulation. Reliab. Eng. Syst. Saf. 212 (2021)
Wensi Wu, Christophe Bonneville, Christopher J. Earls
A Principled Approach to Design Using High Fidelity Fluid-Structure Interaction Simulations. CoRR (2020)
Wensi Wu, Justyna Kosianka, Heather M. Reed, Christopher Stull, Christopher J. Earls
CU-BENs: A structural modeling finite element library. SoftwareX 11 (2020)
Andrew E. Loeb, Christopher J. Earls
Analysis of heterogeneous computing approaches to simulating heat transfer in heterogeneous material. CoRR (2019)
Andrew E. Loeb, Christopher J. Earls
Analysis of heterogeneous computing approaches to simulating heat transfer in heterogeneous material. J. Parallel Distributed Comput. 133 (2019)
Marc Aurèle Gilles, Christopher J. Earls, David Bindel
A Subspace Pursuit Method to Infer Refractivity in the Marine Atmospheric Boundary Layer. IEEE Trans. Geosci. Remote. Sens. 57 (8) (2019)
Vasileios Fountoulakis, Christopher J. Earls
Inverting for Maritime Environments Using Proper Orthogonal Bases From Sparsely Sampled Electromagnetic Propagation Data. IEEE Trans. Geosci. Remote. Sens. 54 (12) (2016)