Christopher J. Earls
Orcid: 0000-0001-8944-5572
According to our database1,
Christopher J. Earls
authored at least 15 papers
between 2016 and 2024.
Collaborative distances:
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Bibliography
2024
LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling law.
CoRR, 2024
2023
Weak-PDE-LEARN: A Weak Form Based Approach to Discovering PDEs From Noisy, Limited Data.
CoRR, 2023
2022
PDE-READ: Human-readable partial differential equation discovery using deep learning.
Neural Networks, 2022
Bayesian deep learning for partial differential equation parameter discovery with sparse and noisy data.
J. Comput. Phys. X, 2022
PDE-LEARN: Using Deep Learning to Discover Partial Differential Equations from Noisy, Limited Data.
CoRR, 2022
2021
SoftwareX, 2021
Deep learning for classifying and characterizing atmospheric ducting within the maritime setting.
Comput. Geosci., 2021
CoRR, 2021
2020
A Principled Approach to Design Using High Fidelity Fluid-Structure Interaction Simulations.
CoRR, 2020
2019
A Subspace Pursuit Method to Infer Refractivity in the Marine Atmospheric Boundary Layer.
IEEE Trans. Geosci. Remote. Sens., 2019
Analysis of heterogeneous computing approaches to simulating heat transfer in heterogeneous material.
J. Parallel Distributed Comput., 2019
2016
Inverting for Maritime Environments Using Proper Orthogonal Bases From Sparsely Sampled Electromagnetic Propagation Data.
IEEE Trans. Geosci. Remote. Sens., 2016