Imagen de Portada

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

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Tauberschmidt, Jan, Fellenz, Sophie, Vollmer, Sebastian J., Duncan, Andrew B.
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Artificial Intelligence Applications
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

MC-GTA: Metric-Constrained Model-Based Clustering using Goodness-of-fit Tests with Autocorrelations
por: Wang, Zhangyu, et al.
Publicado: (2024)

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

KMM-CP: Practical Conformal Prediction under Covariate Shift via Selective Kernel Mean Matching
por: Laghuvarapu, Siddhartha, et al.
Publicado: (2026)

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

Generating Synthetic Relational Tabular Data via Structural Causal Models
por: Hoppe, Frederik, et al.
Publicado: (2025)

Out-of-distribution Reject Option Method for Dataset Shift Problem in Early Disease Onset Prediction
por: Tosaki, Taisei, et al.
Publicado: (2024)

Applying Time Series Deep Learning Models to Forecast the Growth of Perennial Ryegrass in Ireland
por: Onibonoje, Oluwadurotimi, et al.
Publicado: (2025)

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

Feature Group Tabular Transformer: A Novel Approach to Traffic Crash Modeling and Causality Analysis
por: Lares, Oscar, et al.
Publicado: (2024)

General Uncertainty Estimation with Delta Variances
por: Schmitt, Simon, et al.
Publicado: (2025)

How Should We Represent History in Interpretable Models of Clinical Policies?
por: Matsson, Anton, et al.
Publicado: (2024)

Lion Secretly Solves Constrained Optimization: As Lyapunov Predicts
por: Chen, Lizhang, et al.
Publicado: (2023)

Weather-Informed Probabilistic Forecasting and Scenario Generation in Power Systems
por: Zhang, Hanyu, et al.
Publicado: (2024)

Detecting Structural Heart Disease from Electrocardiograms via a Generalized Additive Model of Interpretable Foundation-Model Predictors
por: Zhou, Ya, et al.
Publicado: (2026)

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

PISA: An AI Pipeline for Interpretable-by-design Survival Analysis Providing Multiple Complexity-Accuracy Trade-off Models
por: Schlender, Thalea, et al.
Publicado: (2025)

Personalization of Large Foundation Models for Health Interventions
por: Konigorski, Stefan, et al.
Publicado: (2026)

Explainability of Complex AI Models with Correlation Impact Ratio
por: Sengupta, Poushali, et al.
Publicado: (2026)

Tackling Copyright Issues in AI Image Generation Through Originality Estimation and Genericization
por: Chiba-Okabe, Hiroaki, et al.
Publicado: (2024)

Efficient Prediction of Pass@k Scaling in Large Language Models
por: Kazdan, Joshua, et al.
Publicado: (2025)

Controlling Ensemble Variance in Diffusion Models: An Application for Reanalyses Downscaling
por: Merizzi, Fabio, et al.
Publicado: (2025)

Auto-Regressive Moving Diffusion Models for Time Series Forecasting
por: Gao, Jiaxin, et al.
Publicado: (2024)

Assessing the Operational Viability of Foundation Models for Time Series Forecasting
por: Soni, Kavin, et al.
Publicado: (2026)

Neural Networks with LSTM and GRU in Modeling Active Fires in the Amazon
por: Tavares, Ramon, et al.
Publicado: (2024)

Predicting the Skies: A Novel Model for Flight-Level Passenger Traffic Forecasting
por: Ehsani, Sina, et al.
Publicado: (2024)

Bridging Performance Gaps for ECG Foundation Models: A Post-Training Strategy
por: Zhou, Ya, et al.
Publicado: (2025)

Unleashing The Power of Pre-Trained Language Models for Irregularly Sampled Time Series
por: Zhang, Weijia, et al.
Publicado: (2024)

Learning Explainable Treatment Policies with Clinician-Informed Representations: A Practical Approach
por: Ferstad, Johannes O., et al.
Publicado: (2024)

Large Language Model Predicts Above Normal All India Summer Monsoon Rainfall in 2024
por: Sharma, Ujjawal, et al.
Publicado: (2024)

Enhancing Uplift Modeling in Multi-Treatment Marketing Campaigns: Leveraging Score Ranking and Calibration Techniques
por: Park, Yoon Tae, et al.
Publicado: (2024)

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

Language Models as Causal Effect Generators
por: Bynum, Lucius E. J., et al.
Publicado: (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
por: Lee, Jaewook, et al.
Publicado: (2023)

Estimating Item Difficulty with Large Language Models as Experts
por: Kolesnikova, Diana, et al.
Publicado: (2026)

ConfHit: Conformal Generative Design with Oracle Free Guarantees
por: Laghuvarapu, Siddhartha, et al.
Publicado: (2026)

Learning Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity
por: Jin, Jikai, et al.
Publicado: (2023)

Cross-Domain Energy-Guided Diffusion Generation for Off-Dynamics Reinforcement Learning
por: Yang, Yu, et al.
Publicado: (2026)

Fairboard: a quantitative framework for equity assessment of healthcare models
por: Ruffle, James K., et al.
Publicado: (2026)

Latency-Response Theory Model: Evaluating Large Language Models via Response Accuracy and Chain-of-Thought Length
por: Xu, Zhiyu, et al.
Publicado: (2025)

LLMs for Supply Chain Management
por: Wang, Haojie, et al.
Publicado: (2025)