Generative Models and Statistical Validation
Generative machine learning has become an essential tool in theoretical and experimental physics, especially in the context of fast surrogates and density estimators. In this work, we first introduce the underlying framework of modern generative networks and then discuss challenges in quantifying their accuracy, precision, and statistical power.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Learning to Validate Generative Models: a Goodness-of-Fit Approach
Generative models are increasingly central to scientific workflows, yet their systematic use and interpretation require a proper understanding of their limitations through rigorous validation. Classic approaches struggle…
Generative Adversarial Networks to infer velocity components in rotating turbulent flows
Inference problems for two-dimensional snapshots of rotating turbulent flows are studied. We perform a systematic quantitative benchmark of point-wise and statistical reconstruction capabilities of the linear Extended Pr…
Generative Adversarial NetworkPhysics-informed simulation framework for realistic sonar image generation and statistical validation
Synthetic sonar datasets offer a scalable alternative to costly real-world acquisition, yet their utility remains limited by the absence of rigorous quantitative validation. We present ACOUSIM (ACOustic SIMulation and Va…
Image GenerationA Digital Engineering Approach to Testing Modern AI and Complex Systems
Modern AI (i.e., Deep Learning and its variants) is here to stay. However, its enigmatic black box nature presents a fundamental challenge to the traditional methods of test and validation (T&E). Or does it? In this pape…
Deep LearningWatermarking Generative Categorical Data
In this paper, we propose a novel statistical framework for watermarking generative categorical data. Our method systematically embeds pre-agreed secret signals by splitting the data distribution into two components and …
Synthetic Data Generation