Copertina

Solving physics-based initial value problems with unsupervised machine learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Griffiths, Jack, Wrathmall, Steven A., Gardiner, Simon A.
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Computational Physics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Thermometry of simulated Bose--Einstein condensates using machine learning
di: Griffiths, Jack, et al.
Pubblicazione: (2025)

A physics-informed transformer neural operator for learning generalized solutions of initial boundary value problems
di: Boya, Sumanth Kumar, et al.
Pubblicazione: (2024)

Quantum circuit complexity and unsupervised machine learning of topological order
di: Che, Yanming, et al.
Pubblicazione: (2025)

Indirect measurement of atomic magneto-optical rotation via Hilbert transform
di: Briscoe, Jack D, et al.
Pubblicazione: (2024)

Hybrid ensemble forecasting combining physics-based and machine-learning predictions through spectral nudging
di: Polichtchouk, Inna, et al.
Pubblicazione: (2026)

Using machine learning to find exact analytic solutions to analytically posed physics problems
di: Ashhab, Sahel
Pubblicazione: (2023)

Supervised and unsupervised learning with numerical computation for the Wolfram cellular automata
di: Tuo, Kui, et al.
Pubblicazione: (2025)

Accelerating kinetic plasma simulations with machine learning generated initial conditions
di: Powis, Andrew T., et al.
Pubblicazione: (2025)

Supervised and unsupervised learning of directed percolation
di: Shen, Jianmin, et al.
Pubblicazione: (2021)

CoOMBE: A suite of open-source programs for the integration of the optical Bloch equations and Maxwell-Bloch equations
di: Potvliege, R M, et al.
Pubblicazione: (2024)

Complexity‐based unsupervised machine learning for patient‐specific VMAT quality assurance
di: Savino Cilla, et al.
Pubblicazione: (2025)

Exact symmetry conservation and automatic mesh refinement in discrete initial boundary value problems
di: Rothkopf, Alexander, et al.
Pubblicazione: (2024)

How unconstrained machine-learning models learn physical symmetries
di: Domina, Michelangelo, et al.
Pubblicazione: (2026)

Model fusion with physics-guided machine learning
di: Pawar, Suraj, et al.
Pubblicazione: (2021)

Light propagation through an atomic vapor with non-orthogonal electric field modes
di: Briscoe, Jack D., et al.
Pubblicazione: (2025)

Solving graph problems using permutation-invariant quantum machine learning
di: Mansky, Maximilian Balthasar, et al.
Pubblicazione: (2025)

Inverting the Kohn-Sham equations with physics-informed machine learning
di: Martinetto, Vincent, et al.
Pubblicazione: (2023)

Parallel hybrid quantum-classical machine learning for kernelized time-series classification
di: Baker, Jack S., et al.
Pubblicazione: (2023)

Modular hybrid machine learning and physics-based potentials for scalable modeling of van der Waals heterostructures
di: Bu, Hekai, et al.
Pubblicazione: (2025)

Solving the inverse problem of X-ray absorption spectroscopy via physics-informed deep learning
di: Zhong, Suyang, et al.
Pubblicazione: (2026)

Experimental and theoretical characterisation of Stokes polarimetry of the potassium D1 line with neon buffer gas broadening
di: Alqarni, Sharaa A., et al.
Pubblicazione: (2025)

An inversion problem for optical spectrum data via physics-guided machine learning
di: Park, Hwiwoo, et al.
Pubblicazione: (2024)

PINK: physical-informed machine learning for lattice thermal conductivity
di: Liu, Yujie, et al.
Pubblicazione: (2025)

The Role of Buffer Gas in Shaping the D1 Line Spectrum of Potassium Vapour
di: Alqarni, Sharaa A., et al.
Pubblicazione: (2025)

Physics-tailored machine learning reveals unexpected physics in dusty plasmas
di: Yu, Wentao, et al.
Pubblicazione: (2023)

Integer linear programming for unsupervised training set selection in molecular machine learning
di: Haeberle, Matthieu, et al.
Pubblicazione: (2024)

An iterated learning model of language change that mixes supervised and unsupervised learning
di: Bunyan, Jack, et al.
Pubblicazione: (2024)

The transformative capability of quantum-accurate machine learning interatomic potentials
di: Correa, Alfredo A., et al.
Pubblicazione: (2025)

Self-induced transparency and optical transients in atomic vapors
di: Cartwright, B. S., et al.
Pubblicazione: (2025)

Universal initial state preparation for first quantized quantum simulations
di: Baker, Jack S., et al.
Pubblicazione: (2025)

Classifying acoustic cavitation with machine learning trained on multiple physical models
di: Gatica, Trinidad, et al.
Pubblicazione: (2024)

Understanding multi-fidelity training of machine-learned force-fields
di: Gardner, John L. A., et al.
Pubblicazione: (2025)

More sophisticated is not always better: comparison of similarity measures for unsupervised learning of pathways in biomolecular simulations
di: Jäger, Miriam, et al.
Pubblicazione: (2025)

Inherent structural descriptors via machine learning
di: Telari, Emanuele, et al.
Pubblicazione: (2024)

Exploring quantum localization with machine learning
di: Montes, J., et al.
Pubblicazione: (2024)

Transforming physics-informed machine learning to convex optimization
di: Yi, Letian, et al.
Pubblicazione: (2025)

Discover physical concepts and equations with machine learning
di: Li, Bao-Bing, et al.
Pubblicazione: (2024)

NeuralCrop: Combining physics and machine learning for improved crop yield projections
di: Lin, Yunan, et al.
Pubblicazione: (2025)

Predicting parameters of a model cuprate superconductor using machine learning
di: Ulitko, V. A., et al.
Pubblicazione: (2025)

A general formalism for machine-learning models based on multipolar-spherical harmonics
di: Domina, Michelangelo, et al.
Pubblicazione: (2025)