Learning smooth dynamics via Kinematic Interpolants

Abstract

Inferring dynamics from real-world experimental data is inherently ambiguous, as many plausible dynamical systems can explain the same positional observations at measurement times. For higher fidelity modeling of applications ranging from single-cell RNA to vortex dynamics and even flocking of birds, scientific observables increasingly also include velocity estimates that serve to resolve this issue. However, incorporating this rich information in a scalable manner, free of simulation or unnecessary approximations, has remained a difficult challenge. In this paper, we introduce Kinematic Interpolants (KI), a novel framework that extends the seminal methodology of stochastic interpolants to infer nonlinear dynamics across multiple snapshots in phase space, i.e., observables with position and velocity measurements. Empirically, we demonstrate that across several scientific datasets, KI achieves superior reconstruction of the underlying dynamics compared to existing baselines while requiring only a fraction of the training time.

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Cite

BibTeX RIS

BibTeX

@misc{yiheng2026learning,
  title = {{Learning smooth dynamics via Kinematic Interpolants}},
  author = {Yiheng and Jiang and Youssef Marzouk and Avishek Joey Bose and Aram-Alexandre Pooladian},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.22519},
  eprint = {2610.22519},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.22519},
  note = {CC BY 4.0},
}

RIS

TY  - UNPB
TI  - Learning smooth dynamics via Kinematic Interpolants
AU  - Yiheng
AU  - Jiang
AU  - Youssef Marzouk
AU  - Avishek Joey Bose
AU  - Aram-Alexandre Pooladian
PY  - 2026
DA  - 2026/10/15
PB  - arXiv
DO  - 10.48550/arxiv.2610.22519
UR  - https://arxiv.org/abs/2610.22519
AB  - Inferring dynamics from real-world experimental data is inherently ambiguous, as many plausible dynamical systems can explain the same positional observations at measurement times. For higher fidelity modeling of applications ranging from single-cell RNA to vortex dynamics and even flocking of birds, scientific observables increasingly also include velocity estimates that serve to resolve this issue. However, incorporating this rich information in a scalable manner, free of simulation or unnecessary approximations, has remained a difficult challenge. In this paper, we introduce Kinematic Interpolants (KI), a novel framework that extends the seminal methodology of stochastic interpolants to infer nonlinear dynamics across multiple snapshots in phase space, i.e., observables with position and velocity measurements. Empirically, we demonstrate that across several scientific datasets, KI achieves superior reconstruction of the underlying dynamics compared to existing baselines while requiring only a fraction of the training time.
ER  -

Abstract

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