Thermodynamics-informed super-resolution of scarce temporal dynamics data
Fuente:
arXiv
Saved in:
| Main Authors: | Bermejo-Barbanoj, Carlos, Moya, Beatriz, Badías, Alberto, Chinesta, Francisco, Cueto, Elías |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Graph neural networks informed locally by thermodynamics
by: Tierz, Alicia, et al.
Published: (2024)
by: Tierz, Alicia, et al.
Published: (2024)
A comparison of Single- and Double-generator formalisms for Thermodynamics-Informed Neural Networks
by: Urdeitx, Pau, et al.
Published: (2024)
by: Urdeitx, Pau, et al.
Published: (2024)
Variational Rank Reduction Autoencoders for Generative Thermal Design
by: Tierz, Alicia, et al.
Published: (2025)
by: Tierz, Alicia, et al.
Published: (2025)
Thermodynamics-informed graph neural networks for real-time simulation of digital human twins
by: Tesán, Lucas, et al.
Published: (2024)
by: Tesán, Lucas, et al.
Published: (2024)
Variational Graph Neural Networks for Uncertainty Quantification in Inverse Problems
by: Gonzalez, David, et al.
Published: (2026)
by: Gonzalez, David, et al.
Published: (2026)
Application of Reduced-Order Models for Temporal Multiscale Representations in the Prediction of Dynamical Systems
by: Ghazal, Elias Al, et al.
Published: (2025)
by: Ghazal, Elias Al, et al.
Published: (2025)
Single-snapshot machine learning for super-resolution of turbulence
by: Fukami, Kai, et al.
Published: (2024)
by: Fukami, Kai, et al.
Published: (2024)
Understanding the role of autoencoders for stiff dynamical systems using information theory
by: Vijayarangan, Vijayamanikandan, et al.
Published: (2025)
by: Vijayarangan, Vijayamanikandan, et al.
Published: (2025)
Projection-based multifidelity linear regression for data-scarce applications
by: Sella, Vignesh, et al.
Published: (2025)
by: Sella, Vignesh, et al.
Published: (2025)
Multifidelity linear regression for scientific machine learning from scarce data
by: Qian, Elizabeth, et al.
Published: (2024)
by: Qian, Elizabeth, et al.
Published: (2024)
A Physics-informed Multi-resolution Neural Operator
by: Roy, Sumanta, et al.
Published: (2025)
by: Roy, Sumanta, et al.
Published: (2025)
Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics
by: Liu, Xin-Yang, et al.
Published: (2022)
by: Liu, Xin-Yang, et al.
Published: (2022)
Learning optimal integration of spatial and temporal information in noisy chemotaxis
by: Alonso, Albert, et al.
Published: (2023)
by: Alonso, Albert, et al.
Published: (2023)
Efficient n-body simulations using physics informed graph neural networks
by: Ramos-Osuna, Víctor, et al.
Published: (2025)
by: Ramos-Osuna, Víctor, et al.
Published: (2025)
Physics-informed AI and ML-based sparse system identification algorithm for discovery of PDE's representing nonlinear dynamic systems
by: Pal, Ashish, et al.
Published: (2024)
by: Pal, Ashish, et al.
Published: (2024)
Deep learning for temporal super-resolution 4D Flow MRI
by: Callmer, Pia, et al.
Published: (2025)
by: Callmer, Pia, et al.
Published: (2025)
Transferring self-supervised pre-trained models for SHM data anomaly detection with scarce labeled data
by: Zhou, Mingyuan, et al.
Published: (2024)
by: Zhou, Mingyuan, et al.
Published: (2024)
Boltzmann Classifier: A Thermodynamic-Inspired Approach to Supervised Learning
by: Amin, Muhamed, et al.
Published: (2025)
by: Amin, Muhamed, et al.
Published: (2025)
Distributed physics informed neural network for data-efficient solution to partial differential equations
by: Dwivedi, Vikas, et al.
Published: (2019)
by: Dwivedi, Vikas, et al.
Published: (2019)
Refining Machine Learning Potentials through Thermodynamic Theory of Phase Transitions
by: Fuchs, Paul, et al.
Published: (2025)
by: Fuchs, Paul, et al.
Published: (2025)
Finite basis Kolmogorov-Arnold networks: domain decomposition for data-driven and physics-informed problems
by: Howard, Amanda A., et al.
Published: (2024)
by: Howard, Amanda A., et al.
Published: (2024)
Rapid Bayesian identification of sparse nonlinear dynamics from scarce and noisy data
by: Fung, Lloyd, et al.
Published: (2024)
by: Fung, Lloyd, et al.
Published: (2024)
Active learning of effective Hamiltonian for super-large-scale atomic structures
by: Ma, Xingyue, et al.
Published: (2023)
by: Ma, Xingyue, et al.
Published: (2023)
Physics-informed, Generative Adversarial Design of Funicular Shells
by: Lourenço, Rúben, et al.
Published: (2026)
by: Lourenço, Rúben, et al.
Published: (2026)
Cross-Scale Reservoir Computing for large spatio-temporal forecasting and modeling
by: Alboré, Nicola, et al.
Published: (2025)
by: Alboré, Nicola, et al.
Published: (2025)
Large language models, physics-based modeling, experimental measurements: the trinity of data-scarce learning of polymer properties
by: Liu, Ning, et al.
Published: (2024)
by: Liu, Ning, et al.
Published: (2024)
Deep learning and abstractive summarisation for radiological reports: an empirical study for adapting the PEGASUS models' family with scarce data
by: Benzoni, Claudio, et al.
Published: (2025)
by: Benzoni, Claudio, et al.
Published: (2025)
FFT-based surrogate modeling of auxetic metamaterials with real-time prediction of effective elastic properties and swift inverse design
by: Danesh, Hooman, et al.
Published: (2024)
by: Danesh, Hooman, et al.
Published: (2024)
AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics
by: Mirarchi, Antonio, et al.
Published: (2024)
by: Mirarchi, Antonio, et al.
Published: (2024)
Enforcing hidden physics in physics-informed neural networks
by: Chen, Nanxi, et al.
Published: (2025)
by: Chen, Nanxi, et al.
Published: (2025)
Role of scrambling and noise in temporal information processing with quantum systems
by: Xiong, Weijie, et al.
Published: (2025)
by: Xiong, Weijie, et al.
Published: (2025)
Score dynamics: scaling molecular dynamics with picoseconds timestep via conditional diffusion model
by: Hsu, Tim, et al.
Published: (2023)
by: Hsu, Tim, et al.
Published: (2023)
Efficient physics-informed neural networks using hash encoding
by: Huang, Xinquan, et al.
Published: (2023)
by: Huang, Xinquan, et al.
Published: (2023)
Polyatomic Complexes: A topologically-informed learning representation for atomistic systems
by: Khorana, Rahul, et al.
Published: (2024)
by: Khorana, Rahul, et al.
Published: (2024)
Uncertainties in Physics-informed Inverse Problems: The Hidden Risk in Scientific AI
by: Mototake, Yoh-ichi, et al.
Published: (2025)
by: Mototake, Yoh-ichi, et al.
Published: (2025)
An interpretation of the Brownian bridge as a physics-informed prior for the Poisson equation
by: Alberts, Alex, et al.
Published: (2025)
by: Alberts, Alex, et al.
Published: (2025)
Splitting physics-informed neural networks for inferring the dynamics of integer- and fractional-order neuron models
by: Shekarpaz, Simin, et al.
Published: (2023)
by: Shekarpaz, Simin, et al.
Published: (2023)
Shape-informed cardiac mechanics surrogates in data-scarce regimes via geometric encoding and generative augmentation
by: Carrara, Davide, et al.
Published: (2026)
by: Carrara, Davide, et al.
Published: (2026)
Scalable learning of macroscopic stochastic dynamics
by: Chen, Mengyi, et al.
Published: (2025)
by: Chen, Mengyi, et al.
Published: (2025)
CRADIPOR: Crash Dispersion Predictor
by: Chaillou, Edgar, et al.
Published: (2026)
by: Chaillou, Edgar, et al.
Published: (2026)
Similar Items
-
Graph neural networks informed locally by thermodynamics
by: Tierz, Alicia, et al.
Published: (2024) -
A comparison of Single- and Double-generator formalisms for Thermodynamics-Informed Neural Networks
by: Urdeitx, Pau, et al.
Published: (2024) -
Variational Rank Reduction Autoencoders for Generative Thermal Design
by: Tierz, Alicia, et al.
Published: (2025) -
Thermodynamics-informed graph neural networks for real-time simulation of digital human twins
by: Tesán, Lucas, et al.
Published: (2024) -
Variational Graph Neural Networks for Uncertainty Quantification in Inverse Problems
by: Gonzalez, David, et al.
Published: (2026)