Abstract
This software showcases a latent space dynamics identification method, namely tLaSDI, that embeds the
first and second principles of thermodynamics. The latent variables are learned through an autoencoder
as a nonlinear dimension reduction model. The latent dynamics are constructed by a neural network-based
model that precisely preserves certain structures for the thermodynamic laws through the
GENERIC formalism. An abstract error estimate is established, which provides a new loss formulation
involving the Jacobian computation of autoencoder. The autoencoder and the latent dynamics are
simultaneously trained to minimize the new loss. Computational examples demonstrate the effectiveness
of tLaSDI, which exhibits robust generalization ability, even in extrapolation. In addition, an intriguing
correlation is empirically observed between a quantity from tLaSDI in the latent space and the behaviors
of the full-state solution.
- Developers:
-
Cheung, Siu Wun [1] ; Park, Jun Sur [1] ; Choi, Youngsoo [1] ; Shin, Yeonjong [2]
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- North Carolina State University, Raleigh, NC (United States)
- Release Date:
- 2024-07-31
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Version:
- 0.1
- Licenses:
-
MIT License
- Sponsoring Org.:
-
USDOE National Nuclear Security Administration (NNSA)Primary Award/Contract Number:AC52-07NA27344
- Code ID:
- 142379
- Site Accession Number:
- LLNL-CODE-867909
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Country of Origin:
- United States
Citation Formats
Cheung, Siu Wun, Park, Jun Sur, Choi, Youngsoo, and Shin, Yeonjong.
Thermodynamics-informed latent space dynamics identification.
Computer Software.
https://github.com/pjss1223/tLaSDI.
USDOE National Nuclear Security Administration (NNSA).
31 Jul. 2024.
Web.
doi:10.11578/dc.20240905.1.
Cheung, Siu Wun, Park, Jun Sur, Choi, Youngsoo, & Shin, Yeonjong.
(2024, July 31).
Thermodynamics-informed latent space dynamics identification.
[Computer software].
https://github.com/pjss1223/tLaSDI.
https://doi.org/10.11578/dc.20240905.1.
Cheung, Siu Wun, Park, Jun Sur, Choi, Youngsoo, and Shin, Yeonjong.
"Thermodynamics-informed latent space dynamics identification." Computer software.
July 31, 2024.
https://github.com/pjss1223/tLaSDI.
https://doi.org/10.11578/dc.20240905.1.
@misc{
doecode_142379,
title = {Thermodynamics-informed latent space dynamics identification},
author = {Cheung, Siu Wun and Park, Jun Sur and Choi, Youngsoo and Shin, Yeonjong},
abstractNote = {This software showcases a latent space dynamics identification method, namely tLaSDI, that embeds the
first and second principles of thermodynamics. The latent variables are learned through an autoencoder
as a nonlinear dimension reduction model. The latent dynamics are constructed by a neural network-based
model that precisely preserves certain structures for the thermodynamic laws through the
GENERIC formalism. An abstract error estimate is established, which provides a new loss formulation
involving the Jacobian computation of autoencoder. The autoencoder and the latent dynamics are
simultaneously trained to minimize the new loss. Computational examples demonstrate the effectiveness
of tLaSDI, which exhibits robust generalization ability, even in extrapolation. In addition, an intriguing
correlation is empirically observed between a quantity from tLaSDI in the latent space and the behaviors
of the full-state solution.},
doi = {10.11578/dc.20240905.1},
url = {https://doi.org/10.11578/dc.20240905.1},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20240905.1}},
year = {2024},
month = {jul}
}