# Physics-informed Spline Learning for Nonlinear Dynamics Discovery

@inproceedings{Sun2021PhysicsinformedSL, title={Physics-informed Spline Learning for Nonlinear Dynamics Discovery}, author={Fangzheng Sun and Yang Liu and Hao Sun}, booktitle={IJCAI}, year={2021} }

Dynamical systems are typically governed by a set of linear/nonlinear differential equations. Distilling the analytical form of these equations from very limited data remains intractable in many disciplines such as physics, biology, climate science, engineering and social science. To address this fundamental challenge, we propose a novel Physicsinformed Spline Learning (PiSL) framework to discover parsimonious governing equations for nonlinear dynamics, based on sparsely sampled noisy data. The…

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## 9 Citations

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PIDOC is introduced, by encoding control signal and initial position into the losses of a Physics-Informed Neural Network (PINN), the nonlinear system exhibits the desired route given the control signal.

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