ARXIV:2603.10995 · PHYSICS-INFORMED MODELING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Factorized Neural Implicit DMD for Parametric Dynamics

arXiv

A novel approach to modeling the temporal evolution of physical systems using a factorized neural implicit method.

Blocked on Code›Score4.0Evidence unverified

Opportunity summary

Pain A novel approach to modeling the temporal evolution of physical systems using a factorized neural implicit method.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

A novel approach to modeling the temporal evolution of physical systems using a factorized neural implicit method. Even when physical priors such as partial differential equations are available, such systems often reside in high-dimensional…

METHOD

Full abstract

A data-driven, model-free approach to modeling the temporal evolution of physical systems mitigates the need for explicit knowledge of the governing equations. Even when physical priors such as partial differential equations are available, such systems often reside in high-dimensional state spaces and exhibit nonlinear dynamics, making traditional numerical solvers computationally expensive and ill-suited for real-time analysis and control. Consider the problem of learning a parametric flow of a dynamical system: with an initial field and a set of physical parameters, we aim to predict the system's evolution over time in a way that supports long-horizon rollouts, generalization to unseen parameters, and spectral analysis. We propose a physics-coded neural field parameterization of the Koopman operator's spectral decomposition. Unlike a physics-constrained neural field, which fits a single solution surface, and neural operators, which directly approximate the solution operator at fixed time horizons, our model learns a factorized flow operator that decouples spatial modes and temporal evolution. This structure exposes underlying eigenvalues, modes, and stability of the underlying physical process to enable stable long-term rollouts, interpolation across parameter spaces, and spectral analysis. We demonstrate the efficacy of our method on a range of dynamics problems, showcasing its ability to accurately predict complex spatiotemporal phenomena while providing insights into the system's dynamic behavior.

RESULT

ScienceToStartup currently rates this 4.0/10 on the public viability pass. Consider the problem of learning a parametric flow of a dynamical system: with an initial field and a set of physical parameters, we aim…

WHY NOW

Physics-informed Modeling moved forward this cycle; last verified April 2026. Public score 4.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score4.0

PainA novel approach to modeling the temporal evolution of physical systems using a factorized neural implicit method.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

A novel approach to modeling the temporal evolution of physical systems using a factorized neural implicit method.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2603.10995 · PHYSICS-INFORMED MODELING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Factorized Neural Implicit DMD for Parametric Dynamics

arXiv

A novel approach to modeling the temporal evolution of physical systems using a factorized neural implicit method.

Blocked on Code›Score4.0Evidence unverified

Opportunity summary

Pain A novel approach to modeling the temporal evolution of physical systems using a factorized neural implicit method.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Physics-informed Modeling moved forward this cycle; last verified April 2026. Public score 4.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score4.0

PainA novel approach to modeling the temporal evolution of physical systems using a factorized neural implicit method.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

A novel approach to modeling the temporal evolution of physical systems using a factorized neural implicit method.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2603.10995

Factorized Neural Implicit DMD for Parametric Dynamics

A novel approach to modeling the temporal evolution of physical systems using a factorized neural implicit method.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback33 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

4.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Physics-informed Modeling moved forward this cycle; last verified April 2026. Public score 4.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
A novel approach to modeling the temporal evolution of physical systems using a factorized neural implicit method. Even when physical priors such as partial differential equations are available, such systems often reside in high-dimensional state spaces and exhibit nonlinear dynamics, making traditional numerical solvers computationally expensive and ill-suited for real-time analysis and control.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
A data-driven, model-free approach to modeling the temporal evolution of physical systems mitigates the need for explicit knowledge of the governing equations. Even when physical priors such as partial differential equations are available, such systems often reside in high-dimensional state spaces and exhibit nonlinear dynamics, making traditional numerical solvers computationally expensive and ill-suited for real-time analysis and control.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Consider the problem of learning a parametric flow of a dynamical system: with an initial field and a set of physical parameters, we aim to predict the system's evolution over time in a way that supports long-horizon rollouts, generalization to unseen parameters, and spectral analysis.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Physics-informed Modeling moved forward this cycle; last verified April 2026. Public score 4.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

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Concepts

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Methods

Materials

PDF linked

Markets

Physics-informed Modeling

Competitors

not indexed

Competitive landscape

A novel approach to modeling the temporal evolution of physical systems using a factorized neural implicit method.

Segment

Physics-informed Modeling

Adoption evidence

No public code link in the paper record yet

Commercial read

4.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.10995 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

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PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(33)

ENMA: Tokenwise Autoregression for Generative Neural PDE Operators

2025Armand Kassaï Koupaï, Lise Le Boudec et al.

PDE-Transformer: Efficient and Versatile Transformers for Physics Simulations

2025Benjamin J. Holzschuh, Qiang Liu et al.

DISCO: learning to DISCover an evolution Operator for multi-physics-agnostic prediction

2025Rudy Morel, Jiequn Han et al.

Fast Subspace Fluid Simulation with a Temporally-Aware Basis

2025Siyuan Chen, Yixin Chen et al.

ResKoopNet: Learning Koopman Representations for Complex Dynamics with Spectral Residuals

2025Yuanchao Xu, K. Shao et al.

Zebra: In-Context Generative Pretraining for Solving Parametric PDEs

2024Louis Serrano, Armand Kassaï Koupaï et al.

Koopman Autoencoder Via Singular Value Decomposition for Data-Driven Long-Term Prediction

2024Jinho Choi, S. Krishnan et al.

Koopman operator learning using invertible neural networks

2023Yuhuang Meng, Jian-Kai Huang et al.

Towards Stability of Autoregressive Neural Operators

2023Michael McCabe, P. Harrington et al.

Message Passing Neural PDE Solvers

2022Johannes Brandstetter, Daniel E. Worrall et al.

A dynamic mode decomposition extension for the forecasting of parametric dynamical systems

2021Francesco Andreuzzi, N. Demo et al.

Bagging, optimized dynamic mode decomposition for robust, stable forecasting with spatial and temporal uncertainty quantification

2021Diya Sashidhar, J. Kutz

Modern Koopman Theory for Dynamical Systems

2021S. Brunton, M. Budišić et al.

Non-Linear Partial Differential Equations

2020

Fourier Neural Operator for Parametric Partial Differential Equations

2020Zong-Yi Li, Nikola B. Kovachki et al.

Spectral analysis of the Koopman operator for partial differential equations.

2020H. Nakao, I. Mezić

Forecasting Sequential Data using Consistent Koopman Autoencoders

2020Omri Azencot, N. Benjamin Erichson et al.

Dynamic Mode Decomposition for Continuous Time Systems with the Liouville Operator

2019Joel A. Rosenfeld, R. Kamalapurkar et al.

Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

2019Lu Lu, Pengzhan Jin et al.

Learning Neural PDE Solvers with Convergence Guarantees

2019Jun-Ting Hsieh, Shengjia Zhao et al.

Showing 20 of 33 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkAdaptive Diffusion Posterior Sampling for Data and Model Fusion of Complex Nonlinear Dynamical Systems

4.0

Extension

Builds On ThisAutoregression-Free Neural Operators for Time-Dependent PDEs

0.0

Builds On ThisOnlyDense: Reduced-Order Modeling for Lagrangian simulation

0.0

Builds On ThisNeural Galerkin Normalizing Flow for Transition Probability Density Functions of Diffusion Models

3.0

Commercially relevant

Higher ViabilityA neural operator framework for data-driven discovery of stability and receptivity in physical systems

7.0

Higher ViabilityDisentangled Latent Dynamics Manifold Fusion for Solving Parameterized PDEs

7.0

Higher ViabilityPhysics-integrated neural differentiable modeling for immersed boundary systems

7.0

Higher ViabilityPhysics-informed neural operator for predictive parametric phase-field modelling

6.0

Higher ViabilityPhysics-informed diffusion models in spectral space

6.0

Higher ViabilityProject and Generate: Divergence-Free Neural Operators for Incompressible Flows

7.0

Conflicting

none indexed

Owned Distribution

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Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2603.10995
Route: /paper/factorized-neural-implicit-dmd-for-parametric-dynamics
Active tab: read
Artifact: factorized-neural-implicit-dmd-for-parametric-dynamics

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/factorized-neural-implicit-dmd-for-parametric-dynamics/paper-pack
REST build passport API/api/v1/paper/factorized-neural-implicit-dmd-for-parametric-dynamics/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

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Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/factorized-neural-implicit-dmd-for-parametric-dynamics

stale

Proof freshness: stale
Proof status: unverified
Display score: 4/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

Factorized Neural Implicit DMD for Parametric Dynamics

Canonical ID factorized-neural-implicit-dmd-for-parametric-dynamics | Route /paper/factorized-neural-implicit-dmd-for-parametric-dynamics

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/factorized-neural-implicit-dmd-for-parametric-dynamics

MCP example

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}

source_context

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Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Not build-ready: Factorized Neural Implicit DMD for Parametric Dynamics

/buildability/factorized-neural-implicit-dmd-for-parametric-dynamics

Ignoreblocked

Subject: Factorized Neural Implicit DMD for Parametric Dynamics

Verdict

Ignore

Verdict is Ignore because current viability and proof state do not clear the buildability gate.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/factorized-neural-implicit-dmd-for-parametric-dynamics

Paper ref

factorized-neural-implicit-dmd-for-parametric-dynamics

arXiv id

2603.10995

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

0b38171504f8e7fb47ae78f7009c7b5c3f8cb2fa24e4ebcab831e993d76edd23

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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Factorized Neural Implicit DMD for Parametric Dynamics

Factorized Neural Implicit DMD for Parametric Dynamics

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