ArticleNature communications2024
Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- An optimal Petrov-Galerkin framework for operator networks.Computer methods in applied mechanics and engineering · 2026Article
- AI models of unstable flow exhibit hallucination.Scientific reports · 2026Article
- Learning continuous activation fields from microscopic SEM images of lycoperdioid fungi via CNN-guided neural operator modeling.Scientific reports · 2026Article
- A novel spatiotemporal decomposition and identification of sparse equations for human brain deformation.Scientific reports · 2026Article
- Adjoint method-based Fourier neural operator surrogate solver for wavefront shaping in tunable metasurfaces.iScience · 2025Article
- Monthly climate prediction using deep convolutional neural network and long short-term memory.Scientific reports · 2024Article
- Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems.Nature communications · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Predicting complex dynamics in physical applications governed by partial differential equations in real-time is nearly impossible with traditional numerical simulations due to high computational cost. Neural operators offer a solution by approximating mappings between infinite-dimensional Banach spaces, yet their performance degrades with system size and complexity. We propose an approach for learning neural operators in latent spaces, facilitating real-time predictions for highly nonlinear and multiscale systems on high-dimensional domains. Our method utilizes the deep operator network architecture on a low-dimensional latent space to efficiently approximate underlying operators. Demonstrations on material fracture, fluid flow prediction, and climate modeling highlight superior prediction accuracy and computational efficiency compared to existing methods. Notably, our approach enables approximating large-scale atmospheric flows with millions of degrees, enhancing weather and climate forecasts. Here we show that the proposed approach enables real-time predictions that can facilitate decision-making for a wide range of applications in science and engineering.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.