Data to 7 Oct 2026

Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flow

arXiv 1802.10123 ↗First posted 27 Feb 2018cs.LG cs.GRcited by 97

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Abstract (arXiv)

We propose a method for the data-driven inference of temporal evolutions of physical functions with deep learning. More specifically, we target fluid flows, i.e. Navier-Stokes problems, and we propose a novel LSTM-based approach to predict the changes of pressure fields over time. The central challenge in this context is the high dimensionality of Eulerian space-time data sets. We demonstrate for the first time that dense 3D+time functions of physics system can be predicted within the latent spaces of neural networks, and we arrive at a neural-network based simulation algorithm with significant practical speed-ups. We highlight the capabilities of our method with a series of complex liquid simulations, and with a set of single-phase buoyancy simulations. With a set of trained networks, our method is more than two orders of magnitudes faster than a traditional pressure solver. Additionally, we present and discuss a series of detailed evaluations for the different components of our algorithm.

Key facts. Cited by 97 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Feb 2018.

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Citing papers per year: 2021: 22; 2022: 17; 2023: 21; 2024: 14; 2025: 12; 2026: 11.

97 papers.

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2022A Physics-informed Diffusion Model for High-fidelity Flow Field Reconstructioncited by 150
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2023Machine learning with data assimilation and uncertainty quantification for dynamical systems: a reviewcited by 74
2023Benchmarking Autoregressive Conditional Diffusion Models for Turbulent Flow Simulationcited by 65
2021Physics-Guided Deep Learning for Dynamical Systems: A Surveycited by 43
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2022Learning to Accelerate Partial Differential Equations via Latent Global Evolutioncited by 38
2022Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal datacited by 37

38 more

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2024Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operatorscited by 25
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2024Generative AI for Visualization: State of the Art and Future Directionscited by 23
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2025Lost in Latent Space: An Empirical Study of Latent Diffusion Models for Physics Emulationcited by 19
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2022A Thermodynamics-informed Active Learning Approach to Perception and Reasoning about Fluidscited by 9
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2021A spatio-temporal LSTM model to forecast across multiple temporal and spatial scalescited by 6
2022IDLat: An Importance-Driven Latent Generation Method for Scientific Datacited by 6
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2024Human Motion Prediction under Unexpected Perturbationcited by 5
2021Towards high-accuracy deep learning inference of compressible turbulent flows over aerofoilscited by 4
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