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ARXIV:2602.05416 · ENVIRONMENTAL MODELING · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE
ARXIV:2602.05416ENVIRONMENTAL MODELINGSUBMITTED 19 MAR · 18:48 UTCFRESHNESS STALEarXiv
Develop a Koopman autoencoder-based surrogate for fast and accurate coastal-ocean modeling enabling long-term predictions.
Opportunity summary
Pain Develop a Koopman autoencoder-based surrogate for fast and accurate coastal-ocean modeling enabling long-term predictions.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
Develop a Koopman autoencoder-based surrogate for fast and accurate coastal-ocean modeling enabling long-term predictions. This paper introduces a flexible Koopman autoencoder formulation that incorporates meteorological forcings and boundary conditions, and systematically compares its performance…
While POD-based surrogates are widely explored for hydrodynamic applications, the use of Koopman Autoencoders for real-world coastal-ocean modelling remains relatively limited. This paper introduces a flexible Koopman autoencoder formulation that incorporates meteorological forcings and…
ScienceToStartup currently rates this 2.0/10 on the public viability pass. These error levels, corresponding to a few centimeters, are acceptable for many practical applications, while inference speed-ups of 300-1400x enables workflows such as ensemble…
Environmental Modeling moved forward this cycle; last verified April 2026. Public score 2.0/10.
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Develop a Koopman autoencoder-based surrogate for fast and accurate coastal-ocean modeling enabling long-term predictions.
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10.48550/arXiv.2602.05416Develop a Koopman autoencoder-based surrogate for fast and accurate coastal-ocean modeling enabling long-term predictions.
Abstract
While POD-based surrogates are widely explored for hydrodynamic applications, the use of Koopman Autoencoders for real-world coastal-ocean modelling remains relatively limited. This paper introduces a flexible Koopman autoencoder formulation that incorporates meteorological forcings and boundary conditions, and systematically compares its performance against POD-based surrogates. The Koopman autoencoder employs a learned linear temporal operator in latent space, enabling eigenvalue regularization to promote temporal stability. This strategy is evaluated alongside temporal unrolling techniques for achieving stable and accurate long-term predictions. The models are assessed on three test cases spanning distinct dynamical regimes, with prediction horizons up to one year at 30-minute temporal resolution. Across all cases, the Koopman autoencoder with temporal unrolling yields the best overall accuracy compared to the POD-based surrogates, achieving relative root-mean-squared-errors of 0.01-0.13 and $R^2$-values of 0.65-0.996. Prediction errors are largest for current velocities, and smallest for water surface elevations. Comparing to in-situ observations, the surrogate yields -0.65% to 12% change in water surface elevation prediction error when compared to prediction errors of the physics-based model. These error levels, corresponding to a few centimeters, are acceptable for many practical applications, while inference speed-ups of 300-1400x enables workflows such as ensemble forecasting and long climate simulations for coastal-ocean modelling.
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PROBLEM
Develop a Koopman autoencoder-based surrogate for fast and accurate coastal-ocean modeling enabling long-term predictions. This paper introduces a flexible Koopman autoencoder formulation that incorporates meteorological forcings and boundary conditions, and systematically compa...
METHOD
While POD-based surrogates are widely explored for hydrodynamic applications, the use of Koopman Autoencoders for real-world coastal-ocean modelling remains relatively limited. This paper introduces a flexible Koopman autoencoder formulation that incorporates meteorological forc...
RESULT
ScienceToStartup currently rates this 2.0/10 on the public viability pass. These error levels, corresponding to a few centimeters, are acceptable for many practical applications, while inference speed-ups of 300-1400x enables workflows such as ensemble forecasting and long clima...
WHY NOW
Environmental Modeling moved forward this cycle; last verified April 2026. Public score 2.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Develop a Koopman autoencoder-based surrogate for fast and accurate coastal-ocean modeling enabling long-term predictions. This paper introduces a flexible Koopman autoencoder formulation that incorporates meteorological forcings and boundary conditions, and systematically compares its performance against POD-based surrogates.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
While POD-based surrogates are widely explored for hydrodynamic applications, the use of Koopman Autoencoders for real-world coastal-ocean modelling remains relatively limited. This paper introduces a flexible Koopman autoencoder formulation that incorporates meteorological forcings and boundary conditions, and systematically compares its performance against POD-based surrogates.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 2.0/10 on the public viability pass. These error levels, corresponding to a few centimeters, are acceptable for many practical applications, while inference speed-ups of 300-1400x enables workflows such as ensemble forecasting and long climate simulations for coastal-ocean modelling.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Environmental Modeling moved forward this cycle; last verified April 2026. Public score 2.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Develop a Koopman autoencoder-based surrogate for fast and accurate coastal-ocean modeling enabling long-term predictions.
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