Image de couverture de livre

Physics-Constrained Fine-Tuning of Flow-Matching Models for Generation and Inverse Problems

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Tauberschmidt, Jan, Fellenz, Sophie, Vollmer, Sebastian J., Duncan, Andrew B.
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning Artificial Intelligence Applications
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

MC-GTA: Metric-Constrained Model-Based Clustering using Goodness-of-fit Tests with Autocorrelations
par: Wang, Zhangyu, et autres
Publié: (2024)

Conformal Safety Monitoring for Flight Testing: A Case Study in Data-Driven Safety Learning
par: Feldman, Aaron O., et autres
Publié: (2025)

KMM-CP: Practical Conformal Prediction under Covariate Shift via Selective Kernel Mean Matching
par: Laghuvarapu, Siddhartha, et autres
Publié: (2026)

Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series
par: Brown, Zachary C., et autres
Publié: (2025)

Generating Synthetic Relational Tabular Data via Structural Causal Models
par: Hoppe, Frederik, et autres
Publié: (2025)

Out-of-distribution Reject Option Method for Dataset Shift Problem in Early Disease Onset Prediction
par: Tosaki, Taisei, et autres
Publié: (2024)

Applying Time Series Deep Learning Models to Forecast the Growth of Perennial Ryegrass in Ireland
par: Onibonoje, Oluwadurotimi, et autres
Publié: (2025)

A More Realistic Evaluation of Cross-Frequency Transfer Learning and Foundation Forecasting Models
par: Olivares, Kin G., et autres
Publié: (2025)

Feature Group Tabular Transformer: A Novel Approach to Traffic Crash Modeling and Causality Analysis
par: Lares, Oscar, et autres
Publié: (2024)

General Uncertainty Estimation with Delta Variances
par: Schmitt, Simon, et autres
Publié: (2025)

How Should We Represent History in Interpretable Models of Clinical Policies?
par: Matsson, Anton, et autres
Publié: (2024)

Lion Secretly Solves Constrained Optimization: As Lyapunov Predicts
par: Chen, Lizhang, et autres
Publié: (2023)

Weather-Informed Probabilistic Forecasting and Scenario Generation in Power Systems
par: Zhang, Hanyu, et autres
Publié: (2024)

Detecting Structural Heart Disease from Electrocardiograms via a Generalized Additive Model of Interpretable Foundation-Model Predictors
par: Zhou, Ya, et autres
Publié: (2026)

Beyond Words: How Large Language Models Perform in Quantitative Management Problem-Solving
par: Kuzmanko, Jonathan
Publié: (2025)

PISA: An AI Pipeline for Interpretable-by-design Survival Analysis Providing Multiple Complexity-Accuracy Trade-off Models
par: Schlender, Thalea, et autres
Publié: (2025)

Personalization of Large Foundation Models for Health Interventions
par: Konigorski, Stefan, et autres
Publié: (2026)

Explainability of Complex AI Models with Correlation Impact Ratio
par: Sengupta, Poushali, et autres
Publié: (2026)

Tackling Copyright Issues in AI Image Generation Through Originality Estimation and Genericization
par: Chiba-Okabe, Hiroaki, et autres
Publié: (2024)

Efficient Prediction of Pass@k Scaling in Large Language Models
par: Kazdan, Joshua, et autres
Publié: (2025)

Controlling Ensemble Variance in Diffusion Models: An Application for Reanalyses Downscaling
par: Merizzi, Fabio, et autres
Publié: (2025)

Auto-Regressive Moving Diffusion Models for Time Series Forecasting
par: Gao, Jiaxin, et autres
Publié: (2024)

Assessing the Operational Viability of Foundation Models for Time Series Forecasting
par: Soni, Kavin, et autres
Publié: (2026)

Neural Networks with LSTM and GRU in Modeling Active Fires in the Amazon
par: Tavares, Ramon, et autres
Publié: (2024)

Predicting the Skies: A Novel Model for Flight-Level Passenger Traffic Forecasting
par: Ehsani, Sina, et autres
Publié: (2024)

Bridging Performance Gaps for ECG Foundation Models: A Post-Training Strategy
par: Zhou, Ya, et autres
Publié: (2025)

Unleashing The Power of Pre-Trained Language Models for Irregularly Sampled Time Series
par: Zhang, Weijia, et autres
Publié: (2024)

Learning Explainable Treatment Policies with Clinician-Informed Representations: A Practical Approach
par: Ferstad, Johannes O., et autres
Publié: (2024)

Large Language Model Predicts Above Normal All India Summer Monsoon Rainfall in 2024
par: Sharma, Ujjawal, et autres
Publié: (2024)

Enhancing Uplift Modeling in Multi-Treatment Marketing Campaigns: Leveraging Score Ranking and Calibration Techniques
par: Park, Yoon Tae, et autres
Publié: (2024)

Analyzing the Impact of Climate Change With Major Emphasis on Pollution: A Comparative Study of ML and Statistical Models in Time Series Data
par: Mishra, Anurag, et autres
Publié: (2024)

Language Models as Causal Effect Generators
par: Bynum, Lucius E. J., et autres
Publié: (2024)

Enhancing Data Efficiency and Feature Identification for Lithium-Ion Battery Lifespan Prediction by Deciphering Interpretation of Temporal Patterns and Cyclic Variability Using Attention-Based Models
par: Lee, Jaewook, et autres
Publié: (2023)

Estimating Item Difficulty with Large Language Models as Experts
par: Kolesnikova, Diana, et autres
Publié: (2026)

ConfHit: Conformal Generative Design with Oracle Free Guarantees
par: Laghuvarapu, Siddhartha, et autres
Publié: (2026)

Learning Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity
par: Jin, Jikai, et autres
Publié: (2023)

Cross-Domain Energy-Guided Diffusion Generation for Off-Dynamics Reinforcement Learning
par: Yang, Yu, et autres
Publié: (2026)

Fairboard: a quantitative framework for equity assessment of healthcare models
par: Ruffle, James K., et autres
Publié: (2026)

Latency-Response Theory Model: Evaluating Large Language Models via Response Accuracy and Chain-of-Thought Length
par: Xu, Zhiyu, et autres
Publié: (2025)

LLMs for Supply Chain Management
par: Wang, Haojie, et autres
Publié: (2025)