ArticleNature communications2025
Reduced order modeling with shallow recurrent decoder networks.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Shallow recurrent decoders for neural and behavioural dynamics.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2026Article
- Toward nonlinear representations with Gaussian-splat manifolds for physics-informed learning.Nature communications · 2026Article
- Sparse identification of nonlinear dynamics and Koopman operators with Shallow Recurrent Decoder Networks.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Inverse problems for dynamic patterns in coupled oscillator networks: when larger networks are simpler.Nature communications · 2026Article
- Reduced order modeling with shallow recurrent decoder networks.Nature communications · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Reduced order modeling is of paramount importance for efficiently inferring high-dimensional spatio-temporal fields in parametric contexts. However, conventional dimensionality reduction techniques are typically limited to known and constant parameters, inefficient for nonlinear and chaotic dynamics, and uninformed to the actual system behavior. In this work, we propose a SHallow REcurrent Decoder-based Reduced Order Modeling technique (SHRED-ROM) capable of reconstructing high-dimensional state dynamics in multiple scenarios from the temporal history of limited sensor measurements. To enhance computational efficiency and memory usage, we reduce data dimensionality through data- or physics-driven basis expansions, allowing for compressive training of lightweight networks with minimal hyperparameter tuning. Through applications on chaotic and nonlinear fluid dynamics, we show that SHRED-ROM is a robust decoding-only strategy, capable of dealing with both fixed or mobile sensors, physical and geometrical (possibly time-dependent) parametric dependencies and different data sources, such as high-fidelity simulations, coupled fields and videos, while being agnostic to sensor placement and parameter values.
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Registered trials
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