Data-Driven Learning of Non-Autonomous Systems

Technical Report · Mon Jun 01 04:00:00 EDT 2020

DOI:https://doi.org/10.2172/1763550· OSTI ID:1763550

Qin, Tong ^[1]; Chen, Zhen ^[1]; Jakeman, John Davis ^[2]; Xiu, Dongbin ^[1]

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1. The Ohio State Univ., Columbus, OH (United States)
2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

We present a numerical framework for recovering unknown non-autonomous dynamical systems with time-dependent inputs. To circumvent the difficulty presented by the non-autonomous nature of the system, our method transforms the solution state into piecewise integration of the system over a discrete set of time instances. The time-dependent inputs are then locally parameterized by using a proper model, for example, polynomial regression, in the pieces determined by the time instances. This transforms the original system into a piecewise parametric system that is locally time invariant. We then design a deep neural network structure to learn the local models. Once the network model is constructed, it can be iteratively used over time to conduct global system prediction. We provide theoretical analysis of our algorithm and present a number of numerical examples to demonstrate the effectiveness of the method.

• Research Organization: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); The Ohio State Univ., Columbus, OH (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA); Air Force Office of Scientific Research (AFOSR)
• DOE Contract Number: AC04-94AL85000; NA0003525
• OSTI ID: 1763550
• Report Number(s): SAND--2020-5887R; 686582
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
deep neural network
non-autonomous systems
residual network