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Neural Differential Algebraic Equations
In this work, we show that the proposed NDAEs abstraction is suitable for relevant system-theoretic data-driven modeling tasks.
Neural Lumped Parameter Differential Equations with Application in Friction-Stir Processing
Lumped parameter methods aim to simplify the evolution of spatially-extended or continuous physical systems to that of a "lumped" element representative of the physical scales of the modeled system.
Physics-informed Machine Learning of Parameterized Fundamental Diagrams
For simulated data, we generalize this relationship by introducing contextual information at the learning stage, i. e. vehicle composition, driver behavior, curb zoning configuration, etc, and show how the speed-flow relationship changes as a function of these exogenous factors independent of roadway design.
BIG-bench Machine Learning
Physics-informed machine learning
Structural Inference of Networked Dynamical Systems with Universal Differential Equations
Networked dynamical systems are common throughout science in engineering; e. g., biological networks, reaction networks, power systems, and the like.
Data-Driven Modeling of Nonlinear Traveling Waves
Although traveling waves are readily observed in many physical systems, the underlying governing equations may be unknown.
Fluid Dynamics Dynamical Systems Pattern Formation and Solitons Computational Physics
