Name
Title | ||
|---|---|---|
MIDPhyNet: Memorized infusion of decomposed physics in neural networks to model dynamic systems |
Abstract | ||
|---|---|---|
Integrating simplified or partial physics models with data-driven machine learning models is an emerging concept targeted at facilitating generalizability and extrapolability of complex system behavior predictions. In this paper, we introduce a novel machine learning based fusion model MIDPhyNet that decomposes, memorizes, and integrates first principle physics-based information with data-driven models. In MIDPhyNet the output of partial physics is decomposed into Intrinsic Mode Functions (IMFs), which are then infused to a Memorization Unit to generate embedded vectors. A Prediction Unit synthesizes all of the data to generate prediction results. We test the performance of MIDPhyNet on modeling the behavior of dynamic systems such as an inverted pendulum under wind drag. The results clearly demonstrate the performance benefits of our hybrid architecture over both purely data-driven models and state-of-art hybrid models in terms of generalizability and extrapolability. The MIDPhyNet architecture’s superiority is most significant when the models are trained over sparse data sets and in general, MIDPhyNet provides a generic way to explore how physical information can be infused with data-driven models. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.neucom.2020.11.042 | Neurocomputing |
Keywords | DocType | Volume |
Hybrid model,Physics infused machine learning,Physics guided machine learning,TCN,Empirical mode decomposition | Journal | 428 |
ISSN | Citations | PageRank |
0925-2312 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhibo Zhang | 1 | 2 | 1.06 |
| Rahul Rai | 2 | 28 | 9.38 |
| Souma Chowdhury | 3 | 7 | 7.63 |
| David Doermann | 4 | 4313 | 312.70 |