A Physics-Constrained Neural Differential Equation Framework for Data-Driven Snowpack Simulation
This paper presents a physics-constrained neural differential equation framework for parameterization, and employs it to model the time evolution of seasonal snow depth given hydrometeorological forcings. When trained on data from multiple SNOTEL sites, the parameterization predicts daily snow depth with under 9% median error and Nash Sutcliffe Efficiencies over 0.94 across a wide variety of snow climates. The parameterization also generalizes to new sites not seen during training, which is not often true for calibrated snow models. Requiring the parameterization to predict snow water equivalent in addition to snow depth only increases error to ~12%. The structure of the approach guarantees the satisfaction of physical constraints, enables these constraints during model training, and allows modeling at different temporal resolutions without additional retraining of the parameterization. These benefits hold potential in climate modeling, and could extend to other dynamical systems with physical constraints.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
PINNverse: Accurate parameter estimation in differential equations from noisy data with constrained physics-informed neural networks
Parameter estimation for differential equations from measured data is an inverse problem prevalent across quantitative sciences. Physics-Informed Neural Networks (PINNs) have emerged as effective tools for solving such p…
parameter estimationPhysics-constrained coupled neural differential equations for one dimensional blood flow modeling
Computational cardiovascular flow modeling plays a crucial role in understanding blood flow dynamics. While 3D models provide acute details, they are computationally expensive, especially with fluid-structure interaction…
Computational EfficiencyPhysics-constrained neural differential equations for learning multi-ionic transport
Continuum models for ion transport through polyamide nanopores require solving partial differential equations (PDEs) through complex pore geometries. Resolving spatiotemporal features at this length and time-scale can ma…
Super-resolving sparse observations in partial differential equations: A physics-constrained convolutional neural network approach
We propose the physics-constrained convolutional neural network (PC-CNN) to infer the high-resolution solution from sparse observations of spatiotemporal and nonlinear partial differential equations. Results are shown fo…
Super-ResolutionPhysics-constrained Unsupervised Learning of Partial Differential Equations using Meshes
Enhancing neural networks with knowledge of physical equations has become an efficient way of solving various physics problems, from fluid flow to electromagnetism. Graph neural networks show promise in accurately repres…