Skip to content
DraconiticResearch
Search Sources API
203,234 studies31 sourcesUpdated 7 min ago
PreprintarXiv licencearXiv

Generalized Forcing Method: Generation of Diverse Data for Training Linear Transport PDE Closure Models

  • Wenyuan Xue
  • Ali Mani
Open at sourcePDFDOICite

Abstract

Data-driven closure modeling for transport partial differential equations requires training data that are accurate, affordable, diverse, and directly tailored to the target closure fields. We develop the Generalized Forcing Method (GFM), a data-generation framework for training linear transport closure models. GFM generates such data by running simulations with a zero initial condition and an extra body force that is constructed compatibly with the reduced dynamics. This framework leads to implicit GFM (iGFM), which prescribes resolved trajectories, and explicit GFM (eGFM), which constructs a basis of admissible forcings. We apply eGFM to three linear transport closure problems: homogeneous shear flows, spatially inhomogeneous flows, and homogeneous shear flows with random coefficients. The results show that eGFM can identify accurate and stable reduced models when the reduced variables and model form are consistent with the underlying closure relation.

Versions

Related

  1. Surface-dominant transport in Weyl semimetal NbAs nanowires for next-generation interconnects
    Yeryun Cheon, Mehrdad T. Kiani, Yi-Hsin Tu +23
    SciencePeer-reviewedLicence not statedFull text10 citedAtomic, molecular & optical physicsCondensed matterOpenAlex
  2. Transferable Fast Calorimeter Shower Generation via Multi-Geometry Pre-training
    Thorsten Buss, Henry Day-Hall, Frank Gaede +4
    arXivPreprintCC BY 4.0Full textApplied physics & instrumentationHigh-energy & particle physicsarXiv
  3. Proper generalized decomposition with synthetic preconditioning for multiscale parametric phonon transport
    Jia Liu, Wei Su
    arXivPreprintCC BY 4.0Full textComputational physicsarXiv
  4. Dynamics analysis of entropy generation in magnetized radiative Walters-B hybrid nanofluid flow with dissipative transport over a porous rectangular frame
    Meshal Shutaywi, Muhammad Rooman, Saira yousaf +2
    Physics OpenPeer-reviewedCC BYFull textGeneral physicsDOAJ
  5. Phase-controlled lateral forcing of droplets on a vibrating bath
    Lawrence A. Wilen, Zahra Shamsi, Johnathan Hoggarth +1
    arXivPreprintCC BY-NC-ND 4.0Full textCondensed matterFluid dynamicsarXiv

Cite

BibTeXRIS
BibTeX
@misc{xue2026generalized,
  title = {{Generalized Forcing Method: Generation of Diverse Data for Training Linear Transport PDE Closure Models}},
  author = {Wenyuan Xue and Ali Mani},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2606.05141},
  eprint = {2606.05141},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2606.05141},
}
RIS
TY  - UNPB
TI  - Generalized Forcing Method: Generation of Diverse Data for Training Linear Transport PDE Closure Models
AU  - Wenyuan Xue
AU  - Ali Mani
PY  - 2026
DA  - 2026/09/05
PB  - arXiv
DO  - 10.48550/arxiv.2606.05141
UR  - https://arxiv.org/abs/2606.05141
AB  - Data-driven closure modeling for transport partial differential equations requires training data that are accurate, affordable, diverse, and directly tailored to the target closure fields. We develop the Generalized Forcing Method (GFM), a data-generation framework for training linear transport closure models. GFM generates such data by running simulations with a zero initial condition and an extra body force that is constructed compatibly with the reduced dynamics. This framework leads to implicit GFM (iGFM), which prescribes resolved trajectories, and explicit GFM (eGFM), which constructs a basis of admissible forcings. We apply eGFM to three linear transport closure problems: homogeneous shear flows, spatially inhomogeneous flows, and homogeneous shear flows with random coefficients. The results show that eGFM can identify accurate and stable reduced models when the reduced variables and model form are consistent with the underlying closure relation.
ER  -