Image de couverture de livre

SetPINNs: Set-based Physics-informed Neural Networks

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Nagda, Mayank, Ostheimer, Phil, Specht, Thomas, Rhein, Frank, Jirasek, Fabian, Mandt, Stephan, Kloft, Marius, Fellenz, Sophie
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

PIANO: Physics Informed Autoregressive Network
par: Nagda, Mayank, et autres
Publié: (2025)

HANNA: Hard-constraint Neural Network for Consistent Activity Coefficient Prediction
par: Specht, Thomas, et autres
Publié: (2024)

Tethering Broken Themes: Aligning Neural Topic Models with Labels and Authors
par: Nagda, Mayank, et autres
Publié: (2024)

Sparse Data Diffusion for Scientific Simulations in Biology and Physics
par: Ostheimer, Phil, et autres
Publié: (2025)

Challenging Assumptions in Learning Generic Text Style Embeddings
par: Ostheimer, Phil, et autres
Publié: (2025)

Skipping the Zeros in Diffusion Models for Sparse Data Generation
par: Ostheimer, Phil Sidney, et autres
Publié: (2026)

DiffStyleTS: Diffusion Model for Style Transfer in Time Series
par: Nagda, Mayank, et autres
Publié: (2025)

Comgra: A Tool for Analyzing and Debugging Neural Networks
par: Dietz, Florian, et autres
Publié: (2024)

Continual Neural Topic Model
par: James, Charu Karakkaparambil, et autres
Publié: (2025)

BBPOS: BERT-based Part-of-Speech Tagging for Uzbek
par: Bobojonova, Latofat, et autres
Publié: (2025)

Thermodynamically consistent machine learning model for excess Gibbs energy
par: Hoffmann, Marco, et autres
Publié: (2025)

Early-Exit Neural Networks with Nested Prediction Sets
par: Jazbec, Metod, et autres
Publié: (2023)

Population Risk Bounds for Kolmogorov-Arnold Networks Trained by DP-SGD with Correlated Noise
par: Wang, Puyu, et autres
Publié: (2026)

Advancing Thermodynamic Group-Contribution Methods by Machine Learning: UNIFAC 2.0
par: Hayer, Nicolas, et autres
Publié: (2024)

Multi-level Supervised Contrastive Learning
par: Ghanooni, Naghmeh, et autres
Publié: (2025)

Formally Exploring Time-Series Anomaly Detection Evaluation Metrics
par: Wagner, Dennis, et autres
Publié: (2025)

Towards Graph Foundation Models: A Study on the Generalization of Positional and Structural Encodings
par: Franks, Billy Joe, et autres
Publié: (2024)

MLPROP -- an open interactive web interface for thermophysical property prediction with machine learning
par: Hoffmann, Marco, et autres
Publié: (2025)

Anomaly Detection of Tabular Data Using LLMs
par: Li, Aodong, et autres
Publié: (2024)

Adaptive Interface-PINNs (AdaI-PINNs): An Efficient Physics-informed Neural Networks Framework for Interface Problems
par: Roy, Sumanta, et autres
Publié: (2024)

Reimagining Anomalies: What If Anomalies Were Normal?
par: Liznerski, Philipp, et autres
Publié: (2024)

Neural NeRF Compression
par: Pham, Tuan, et autres
Publié: (2024)

KnowTD-An Actionable Knowledge Representation System for Thermodynamics
par: Vollmer, Luisa, et autres
Publié: (2024)

GRAPPA -- A Hybrid Graph Neural Network for Predicting Pure Component Vapor Pressures
par: Hoffmann, Marco, et autres
Publié: (2025)

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks
par: Wang, Puyu, et autres
Publié: (2026)

E-PINNs: Epistemic Physics-Informed Neural Networks
par: Jacob, Bruno, et autres
Publié: (2025)

Approximating Rayleigh Scattering in Exoplanetary Atmospheres using Physics-informed Neural Networks (PINNs)
par: Dahlbüdding, David, et autres
Publié: (2024)

On the Lipschitz Continuity of Set Aggregation Functions and Neural Networks for Sets
par: Nikolentzos, Giannis, et autres
Publié: (2025)

Feature Mapping in Physics-Informed Neural Networks (PINNs)
par: Zeng, Chengxi, et autres
Publié: (2024)

RRaPINNs: Residual Risk-Aware Physics Informed Neural Networks
par: Akazan, Ange-Clément, et autres
Publié: (2025)

Advances in Diffusion-Based Generative Compression
par: Yang, Yibo, et autres
Publié: (2026)

Labeling Neural Representations with Inverse Recognition
par: Bykov, Kirill, et autres
Publié: (2023)

Lossy Image Compression with Conditional Diffusion Models
par: Yang, Ruihan, et autres
Publié: (2022)

Neural Network Symmetrisation in Concrete Settings
par: Cornish, Rob
Publié: (2024)

Explaining Bayesian Neural Networks
par: Bykov, Kirill, et autres
Publié: (2021)

CHAOS -- A Consistent Large-scale Database for Sigma-Profiles and Other Molecular Descriptors
par: Gond, Dominik, et autres
Publié: (2025)

Credal Concept Bottleneck Models: Structural Separation of Epistemic and Aleatoric Uncertainty
par: Mukherjee, Tanmoy, et autres
Publié: (2026)

Mask-PINNs: Mitigating Internal Covariate Shift in Physics-Informed Neural Networks
par: Jiang, Feilong, et autres
Publié: (2025)

Taylor-Model Physics-Informed Neural Networks (PINNs) for Ordinary Differential Equations
par: Nagesh, Chandra Kanth, et autres
Publié: (2025)

On the Topology of Neural Network Superlevel Sets
par: Gharesifard, Bahman
Publié: (2026)