Related papers: Diffusion model approach to simulating electron-pr…

Diffusion-based generative models have reformed generative AI, and also enabled new capabilities in the science domain, e.g., fast generation of 3D structures of molecules. In such tasks, there is often a symmetry in the system, identifying…

Heavy-ion collision physics has entered the high precision era, demanding theoretical models capable of generating huge statistics to compare with experimental data. However, traditional hybrid models, which combine hydrodynamics and…

Generative artificial intelligence (AI) refers to algorithms that create synthetic but realistic output. Diffusion models currently offer state of the art performance in generative AI for images. They also form a key component in more…

To present a prediction of the multi-particle production and multi-fragment emission in electron-nucleus ($eA$) collisions at the forthcoming Electron-Ion Collider (EIC), a simple hybrid model which is based on the multi-source thermal…

The discovery of inorganic crystal structures with targeted properties is a significant challenge in materials science. Generative models, especially state-of-the-art diffusion models, offer the promise of modeling complex data…

A machine learning technique is used to fit multiplicity distributions in high-energy proton-proton collisions and applied to make predictions for collisions at higher energies. The method is tested with Monte Carlo event generator events.…

Diffusion models are generative models that have shown significant advantages compared to other generative models in terms of higher generation quality and more stable training. However, the computational need for training diffusion models…

We present a new technique to calculate the cross-section for diffractive vector meson production and DVCS in electron-ion collisions based on the dipole model. The measurement of these processes can provide valuable information on…

Diffusion models offer stable training and state-of-the-art performance for deep generative modeling tasks. Here, we consider their use in the context of multivariate subsurface modeling and probabilistic inversion. We first demonstrate…

We investigate the evolution of nuclear excitation in electron-nucleus (e+A) collisions at the upcoming Electron-Ion Collider (EIC) using the BeAGLE event generator. Leveraging the EIC's unique collider kinematics, we demonstrate the…

We discuss the prospects of using jets as precision probes in electron-nucleus collisions at the future Electron-Ion Collider. Jets produced in deep-inelastic scattering can be calibrated by a measurement of the scattered electron. Such…

We train a generative diffusion model (DM) to simulate ultra-relativistic heavy-ion collisions from end to end. The model takes initial entropy density profiles as input and produces two-dimensional final particle spectra, successfully…

Generative AI presents chemists with novel ideas for drug design and facilitates the exploration of vast chemical spaces. Diffusion models (DMs), an emerging tool, have recently attracted great attention in drug R\&D. This paper…

Generative models realized with machine learning techniques are powerful tools to infer complex and unknown data distributions from a finite number of training samples in order to produce new synthetic data. Diffusion models are an emerging…

We present a new machine learning-based Monte Carlo event generator using generative adversarial networks (GANs) that can be trained with calibrated detector simulations to construct a vertex-level event generator free of theoretical…

We present a study for the generation of events from a physical process with deep generative models. The simulation of physical processes requires not only the production of physical events, but also to ensure these events occur with the…

Dynamic contingency screening is a challenging task in dynamic security assessment, when traditional numerical approaches are computationally intensive and often not able to repeatedly solve full AC power flow for all possible contingencies…

We present a novel deep generative framework that uses probabilistic diffusion models for ultra fast, event-by-event simulations of heavy-ion collision output. This new framework is trained on UrQMD cascade data to generate a full collision…

Seismic data processing involves techniques to deal with undesired effects that occur during acquisition and pre-processing. These effects mainly comprise coherent artefacts such as multiples, non-coherent signals such as electrical noise,…

Event skeleton generation, aiming to induce an event schema skeleton graph with abstracted event nodes and their temporal relations from a set of event instance graphs, is a critical step in the temporal complex event schema induction task.…