T
Tom Beucler
Researcher at University of California, Irvine
Publications - 39
Citations - 567
Tom Beucler is an academic researcher from University of California, Irvine. The author has contributed to research in topics: Artificial neural network & Computer science. The author has an hindex of 8, co-authored 24 publications receiving 274 citations. Previous affiliations of Tom Beucler include Columbia University & Massachusetts Institute of Technology.
Papers
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Journal ArticleDOI
Enforcing Analytic Constraints in Neural Networks Emulating Physical Systems.
Tom Beucler,Tom Beucler,Michael S. Pritchard,Stephan Rasp,Jordan Ott,Pierre Baldi,Pierre Gentine +6 more
TL;DR: This work introduces a systematic way of enforcing nonlinear analytic constraints in neural networks via constraints in the architecture or the loss function, which reduces errors in the subsets of the outputs most impacted by the constraints.
Posted Content
Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling
TL;DR: It is shown that architecture constraints can enforce conservation laws to satisfactory numerical precision, while all constraints help the neural-network better generalize to conditions outside of its training set, such as global warming.
Journal ArticleDOI
Interpreting and Stabilizing Machine-learning Parametrizations of Convection
Noah D. Brenowitz,Tom Beucler,Tom Beucler,Michael S. Pritchard,Christopher S. Bretherton,Christopher S. Bretherton +5 more
TL;DR: In this paper, the nonlinear sensitivity of a neural network to lower-tropospheric stability and the mid-troposphere moisture is assessed, and the linearized response functions of these neural networks to simplified gravity-wave dynamics are analyzed.
Journal ArticleDOI
Moisture-radiative cooling instability
Tom Beucler,Timothy W. Cronin +1 more
TL;DR: In this paper, the authors studied the effect of moisture and radiative cooling instability on the development of large-scale circulation in the tropical air, and showed that the potential for unstable growth of moist or dry perturbations is there.
Journal ArticleDOI
Interpreting and Stabilizing Machine-learning Parametrizations of Convection
Noah D. Brenowitz,Tom Beucler,Tom Beucler,Michael S. Pritchard,Christopher S. Bretherton,Christopher S. Bretherton +5 more
TL;DR: In this paper, the nonlinear sensitivity of a neural network to lower-tropospheric stability and the mid-troposphere moisture is assessed, and the linearized response functions of these neural networks to simplified gravity-wave dynamics are analyzed.