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ARXIV:2603.12635 · SURROGATE MODELING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.12635SURROGATE MODELINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
A generative machine learning framework for efficient surrogate modeling of chaotic dynamical systems.
Opportunity summary
Pain A generative machine learning framework for efficient surrogate modeling of chaotic dynamical systems.
Evidence 0 refs | 0 sources | 17% coverage
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A generative machine learning framework for efficient surrogate modeling of chaotic dynamical systems. Most surrogate models for such systems are deterministic, for example when neural operators are involved.
High-fidelity numerical simulations of chaotic, high dimensional nonlinear dynamical systems are computationally expensive, necessitating the development of efficient surrogate models. Most surrogate models for such systems are deterministic, for example when neural operators are…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We present our methodology on two-dimensional homogeneous and isotropic turbulence and for a flow over a backwards-facing step, demonstrating its utility in forecasting, adaptive…
Surrogate Modeling moved forward this cycle; last verified April 2026. Public score 4.0/10.
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A generative machine learning framework for efficient surrogate modeling of chaotic dynamical systems.
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10.48550/arXiv.2603.12635A generative machine learning framework for efficient surrogate modeling of chaotic dynamical systems.
Abstract
High-fidelity numerical simulations of chaotic, high dimensional nonlinear dynamical systems are computationally expensive, necessitating the development of efficient surrogate models. Most surrogate models for such systems are deterministic, for example when neural operators are involved. However, deterministic models often fail to capture the intrinsic distributional uncertainty of chaotic systems. This work presents a surrogate modeling formulation that leverages generative machine learning, where a deep learning diffusion model is used to probabilistically forecast turbulent flows over long horizons. We introduce a multi-step autoregressive diffusion objective that significantly enhances long-rollout stability compared to standard single-step training. To handle complex, unstructured geometries, we utilize a multi-scale graph transformer architecture incorporating diffusion preconditioning and voxel-grid pooling. More importantly, our modeling framework provides a unified platform that also predicts spatiotemporally important locations for sensor placement, either via uncertainty estimates or through an error-estimation module. Finally, the observations of the ground truth state at these dynamically varying sensor locations are assimilated using diffusion posterior sampling requiring no retraining of the surrogate model. We present our methodology on two-dimensional homogeneous and isotropic turbulence and for a flow over a backwards-facing step, demonstrating its utility in forecasting, adaptive sensor placement, and data assimilation for high dimensional chaotic systems.
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PROBLEM
A generative machine learning framework for efficient surrogate modeling of chaotic dynamical systems. Most surrogate models for such systems are deterministic, for example when neural operators are involved.
METHOD
High-fidelity numerical simulations of chaotic, high dimensional nonlinear dynamical systems are computationally expensive, necessitating the development of efficient surrogate models. Most surrogate models for such systems are deterministic, for example when neural operators ar...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We present our methodology on two-dimensional homogeneous and isotropic turbulence and for a flow over a backwards-facing step, demonstrating its utility in forecasting, adaptive sensor placement, and dat...
WHY NOW
Surrogate Modeling moved forward this cycle; last verified April 2026. Public score 4.0/10.
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A generative machine learning framework for efficient surrogate modeling of chaotic dynamical systems. Most surrogate models for such systems are deterministic, for example when neural operators are involved.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
High-fidelity numerical simulations of chaotic, high dimensional nonlinear dynamical systems are computationally expensive, necessitating the development of efficient surrogate models. Most surrogate models for such systems are deterministic, for example when neural operators are involved.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We present our methodology on two-dimensional homogeneous and isotropic turbulence and for a flow over a backwards-facing step, demonstrating its utility in forecasting, adaptive sensor placement, and data assimilation for high dimensional chaotic systems.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Surrogate Modeling moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A generative machine learning framework for efficient surrogate modeling of chaotic dynamical systems.
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