Abstract 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 with scalability, statistical power, or interpretability when applied to high-dimensional data, making it difficult to certify the reliability of these models in realistic, high-dimensional scientific settings. Here, we propose the use of the New Physics Learning Machine (NPLM), a learning-based approach to goodness-of-fit testing inspired by the Neyman–Pearson construction, to test generative networks trained on high-dimensional scientific data. We demonstrate the performance of NPLM for validation in two benchmark cases: generative models trained on mixtures of Gaussian models with increasing dimensionality, and a public end-to-end model developed to generate high-energy physics collision events. We show that NPLM can serve as a powerful validation method while also providing a means to diagnose sub-optimally modeled regions of the data.
Abstract Anomaly detection methods used in a recent search for new phenomena by CMS at the CERN LHC are presented. The methods use machine learning to detect anomalous jets produced in the decay of new massive particles without depending on a specific theory model. The effectiven…
Abstract Modelling fluid dynamics with machine learning (ML) has advanced rapidly, yet most data driven approaches remain opaque because they rely on complex architectures to capture nonlinear flow behaviour. This lack of interpretability limits the reliability and hinders the un…
Abstract Nuclear energy is a clean, reliable power source, but realizing its potential requires strict safety measures in nuclear power plants. Thermal-hydraulic (TH) codes are used to simulate potential accident scenarios in probabilistic safety assessment (PSA). Their high comp…
Abstract The uniformity of the film thickness of large-aperture mirror is a critical factor affecting the imaging quality of reflective optical systems. A deep learning-based mask design strategy is proposed to reduce this non-uniformity. By developing a convolutional neural netw…
Abstract Modeling temporal dependencies within quantum systems remains a key challenge for quantum machine learning. Current quantum neural networks largely depend on classical recurrent modules, which introduce optimization bottlenecks and coherence loss during sequence processi…
Abstract Deep learning has emerged as a key tool for designing nanophotonic structures that manipulates light at sub-wavelength scales. Although a conventional approach of measuring the optical properties of a given nanostructure is conceptually straightforward, inverse design re…