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ARXIV:2603.12676 · PHYSICS-INFORMED LEARNING · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE
ARXIV:2603.12676PHYSICS-INFORMED LEARNINGSUBMITTED 19 MAR · 18:48 UTCFRESHNESS STALEarXiv
DLDMF is a physics-informed framework that enhances neural surrogate models for parameterized PDEs by separating space, time, and parameters for improved generalization and extrapolation.
Opportunity summary
Pain DLDMF is a physics-informed framework that enhances neural surrogate models for parameterized PDEs by separating space, time, and parameters for improved generalization and extrapolation.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
DLDMF is a physics-informed framework that enhances neural surrogate models for parameterized PDEs by separating space, time, and parameters for improved generalization and extrapolation. The problem becomes even more severe when the model must…
Generalizing neural surrogate models across different PDE parameters remains difficult because changes in PDE coefficients often make learning harder and optimization less stable. The problem becomes even more severe when the model must also…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. As a result, it performs well on unseen parameter settings and in long-term temporal extrapolation.
Physics-Informed Learning moved forward this cycle; last verified April 2026. Public score 7.0/10.
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DLDMF is a physics-informed framework that enhances neural surrogate models for parameterized PDEs by separating space, time, and parameters for improved generalization and extrapolation.
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10.48550/arXiv.2603.12676DLDMF is a physics-informed framework that enhances neural surrogate models for parameterized PDEs by separating space, time, and parameters for improved generalization and extrapolation.
Abstract
Generalizing neural surrogate models across different PDE parameters remains difficult because changes in PDE coefficients often make learning harder and optimization less stable. The problem becomes even more severe when the model must also predict beyond the training time range. Existing methods usually cannot handle parameter generalization and temporal extrapolation at the same time. Standard parameterized models treat time as just another input and therefore fail to capture intrinsic dynamics, while recent continuous-time latent methods often rely on expensive test-time auto-decoding for each instance, which is inefficient and can disrupt continuity across the parameterized solution space. To address this, we propose Disentangled Latent Dynamics Manifold Fusion (DLDMF), a physics-informed framework that explicitly separates space, time, and parameters. Instead of unstable auto-decoding, DLDMF maps PDE parameters directly to a continuous latent embedding through a feed-forward network. This embedding initializes and conditions a latent state whose evolution is governed by a parameter-conditioned Neural ODE. We further introduce a dynamic manifold fusion mechanism that uses a shared decoder to combine spatial coordinates, parameter embeddings, and time-evolving latent states to reconstruct the corresponding spatiotemporal solution. By modeling prediction as latent dynamic evolution rather than static coordinate fitting, DLDMF reduces interference between parameter variation and temporal evolution while preserving a smooth and coherent solution manifold. As a result, it performs well on unseen parameter settings and in long-term temporal extrapolation. Experiments on several benchmark problems show that DLDMF consistently outperforms state-of-the-art baselines in accuracy, parameter generalization, and extrapolation robustness.
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PROBLEM
DLDMF is a physics-informed framework that enhances neural surrogate models for parameterized PDEs by separating space, time, and parameters for improved generalization and extrapolation. The problem becomes even more severe when the model must also predict beyond the training t...
METHOD
Generalizing neural surrogate models across different PDE parameters remains difficult because changes in PDE coefficients often make learning harder and optimization less stable. The problem becomes even more severe when the model must also predict beyond the training time rang...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. As a result, it performs well on unseen parameter settings and in long-term temporal extrapolation.
WHY NOW
Physics-Informed Learning moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
DLDMF is a physics-informed framework that enhances neural surrogate models for parameterized PDEs by separating space, time, and parameters for improved generalization and extrapolation. The problem becomes even more severe when the model must also predict beyond the training time range.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Generalizing neural surrogate models across different PDE parameters remains difficult because changes in PDE coefficients often make learning harder and optimization less stable. The problem becomes even more severe when the model must also predict beyond the training time range.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. As a result, it performs well on unseen parameter settings and in long-term temporal extrapolation.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Physics-Informed Learning moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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DLDMF is a physics-informed framework that enhances neural surrogate models for parameterized PDEs by separating space, time, and parameters for improved generalization and extrapolation.
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Physics-Informed Learning
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