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Christopher J. Earls
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2020 – today
- 2024
- [j11]Robert Stephany
, Christopher J. Earls:
Weak-PDE-LEARN: A weak form based approach to discovering PDEs from noisy, limited data. J. Comput. Phys. 506: 112950 (2024) - [j10]Robert Stephany
PDE-LEARN: Using deep learning to discover partial differential equations from noisy, limited data. Neural Networks 174: 106242 (2024) - [c1]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. EMNLP 2024: 15097-15117 - [i11]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 abs/2402.00795 (2024) - [i10]Raphaël Sarfati, Toni J. B. Liu, Nicolas Boullé, Christopher J. Earls:
Lines of Thought in Large Language Models. CoRR abs/2410.01545 (2024) - 2023
- [i9]Robert Stephany, Christopher J. Earls:
Weak-PDE-LEARN: A Weak Form Based Approach to Discovering PDEs From Noisy, Limited Data. CoRR abs/2309.04699 (2023) - 2022
- [j9]Christophe Bonneville
Bayesian deep learning for partial differential equation parameter discovery with sparse and noisy data. J. Comput. Phys. X 16: 100115 (2022) - [j8]Robert Stephany
PDE-READ: Human-readable partial differential equation discovery using deep learning. Neural Networks 154: 360-382 (2022) - [i8]Harshwardhan Praveen, Nicolas Boullé, Christopher J. Earls
Principled interpolation of Green's functions learned from data. CoRR abs/2211.06299 (2022) - [i7]Robert Stephany, Christopher J. Earls
PDE-LEARN: Using Deep Learning to Discover Partial Differential Equations from Noisy, Limited Data. CoRR abs/2212.04971 (2022) - 2021
- [j7]Hilarie Sit
Deep learning for classifying and characterizing atmospheric ducting within the maritime setting. Comput. Geosci. 157: 104919 (2021) - [j6]Christophe Bonneville
Gaussian processes for shock test emulation. Reliab. Eng. Syst. Saf. 212: 107624 (2021) - [j5]John Taylor Chavis III
CU-MSDSp: A flexible parallelized Reversible jump Markov chain Monte Carlo method. SoftwareX 14: 100664 (2021) - [i6]Wensi Wu, Christopher J. Earls:
A new engineering theory describing oblique free surface impact by flexible plates. CoRR abs/2103.08012 (2021) - [i5]Nicolas Boullé
Data-driven discovery of physical laws with human-understandable deep learning. CoRR abs/2105.00266 (2021) - [i4]Christophe Bonneville, Christopher J. Earls
Bayesian Deep Learning for Partial Differential Equation Parameter Discovery with Sparse and Noisy Data. CoRR abs/2108.04085 (2021) - [i3]Robert Stephany, Christopher J. Earls
PDE-READ: Human-readable Partial Differential Equation Discovery using Deep Learning. CoRR abs/2111.00998 (2021) - 2020
- [j4]Wensi Wu
CU-BENs: A structural modeling finite element library. SoftwareX 11: 100485 (2020) - [i2]Wensi Wu, Christophe Bonneville, Christopher J. Earls:
A Principled Approach to Design Using High Fidelity Fluid-Structure Interaction Simulations. CoRR abs/2008.09687 (2020)
2010 – 2019
- 2019
- [j3]Andrew E. Loeb, Christopher J. Earls
Analysis of heterogeneous computing approaches to simulating heat transfer in heterogeneous material. J. Parallel Distributed Comput. 133: 1-17 (2019) - [j2]Marc Aurèle Gilles
A Subspace Pursuit Method to Infer Refractivity in the Marine Atmospheric Boundary Layer. IEEE Trans. Geosci. Remote. Sens. 57(8): 5606-5617 (2019) - [i1]Andrew E. Loeb, Christopher J. Earls:
Analysis of heterogeneous computing approaches to simulating heat transfer in heterogeneous material. CoRR abs/1905.07622 (2019) - 2016
- [j1]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): 7166-7176 (2016)
Coauthor Index
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