Path-minimizing latent ODEs for improved extrapolation and inference Article Swipe
Matt L. Sampson
,
P. Melchior
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1088/2632-2153/addc34
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1088/2632-2153/addc34
Latent ordinary differential equation (ODE) models provide flexible descriptions of dynamic systems, but they can struggle with extrapolation and predicting complicated non-linear dynamics. The latent ODE approach implicitly relies on encoders to identify unknown system parameters and initial conditions (ICs), whereas the evaluation times are known and directly provided to the ODE solver. This dichotomy can be exploited by encouraging time-independent latent representations. By replacing the common variational penalty in latent space with an penalty on the path length of each system, the models learn data representations that can easily be distinguished from those of systems with different configurations. This results in faster training, smaller models, more accurate interpolation and long-time extrapolation compared to the baseline ODE models with a gated recurent unit (GRU), recurrent neural network, and long-short-term-memory encoder/decoders on tests with damped harmonic oscillator, self-gravitating fluid, and predator-prey systems. We also demonstrate superior results for simulation-based inference of the Lotka–Volterra parameters and ICs by using the latents as data summaries for a conditional normalizing flow. Our change to the training loss is agnostic to the specific recognition network used by the decoder and can therefore easily be adopted by other latent ODE models.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/2632-2153/addc34
- OA Status
- gold
- References
- 11
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410610778
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https://openalex.org/W4410610778Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/2632-2153/addc34Digital Object Identifier
- Title
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Path-minimizing latent ODEs for improved extrapolation and inferenceWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-22Full publication date if available
- Authors
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Matt L. Sampson, P. MelchiorList of authors in order
- Landing page
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https://doi.org/10.1088/2632-2153/addc34Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.1088/2632-2153/addc34Direct OA link when available
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Extrapolation, Inference, Ode, Path (computing), Computer science, Applied mathematics, Mathematics, Mathematical optimization, Machine learning, Artificial intelligence, Statistics, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.<mml:mi>ℓ</mml:mi> | 80 |
| abstract_inverted_index.long-short-term-memory | 139 |
| abstract_inverted_index.xmlns:mml="http://www.w3.org/1998/Math/MathML" | 76 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.24629325 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |