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Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems
Tom Beucler, Michael Pritchard, Stephan Rasp +3
Neural networks can emulate nonlinear physical systems with high accuracy, yet they may produce physically-inconsistent results when violating fundamental constraints. Here, we int…
Comparing Convective Self-Aggregation in Idealized Models to Observed Moist Static Energy Variability near the Equator
Tom Beucler, Tristan Abbott, Timothy Cronin +1
Idealized convection-permitting simulations of radiative-convective equilibrium (RCE) have become a popular tool for understanding the physical processes leading to horizontal vari…
Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling
Tom Beucler, Stephan Rasp, Michael Pritchard +1
Artificial neural-networks have the potential to emulate cloud processes with higher accuracy than the semi-empirical emulators currently used in climate models. However, neural-ne…