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The conformational landscape of proteins is crucial to understanding their functionality in complex biological processes. Traditional physics-based computational methods, such as molecular dynamics (MD) simulations, suffer from rare event…

Efficient exploration of the vast chemical space is a fundamental challenge in materials design and discovery, particularly for designing functional inorganic crystalline materials with targeted properties. Diffusion-based generative models…

We demonstrate that a future high-energy electron-proton collider like THERA could largely extend the current HERA program in jet photoproduction of testing QCD and determining the partonic structure of the proton and the photon. Depending…

In this study, a novel approach is demonstrated for converting calorimeter images from fast simulations to those akin to comprehensive full simulations, utilizing conditional Generative Adversarial Networks (GANs). The concept of pix2pix is…

Generative AI models, such as score-based diffusion models, have recently advanced the field of computational materials science by enabling the generation of new materials with desired properties. In addition, these models could also be…

High Energy Jets (HEJ) is a new framework for approximating the all-order perturbative corrections to multi-jet processes, with a focus on the hard, wide-angle QCD emissions, which underpins the perturbative description of hard jets. In…

Methods for processing point cloud information have seen a great success in collider physics applications. One recent breakthrough in machine learning is the usage of Transformer networks to learn semantic relationships between sequences in…

Synthetic vehicle speed trajectory generation is essential for evaluating vehicle control algorithms and connected vehicle technologies. Traditional Markov chain approaches suffer from discretization artifacts and limited expressiveness.…

Jets with transverse energy of few TeV are becoming now common in LHC data. Most of these jets are produced by QCD processes and some from the collimated decay of highly boosted objects like W, Z, H0 and top-quark. The study of such QCD…

Parameter estimation, which represents a classical inverse problem, is often ill-posed as different parameter combinations can yield identical outputs. This non-uniqueness poses a critical barrier to accurate and unique identification. This…

In this paper we use our previously developed projective phase space generator for the calculation of the hadronic production of a vector boson with one additional jet at Next-to-Leading Order. The projective phase space generator allows us…

The particle flow approach to calorimetry can provide unprecedented jet energy resolution at a future high energy collider, such as the International Linear Collider (ILC). However, the use of particle flow calorimetry at the proposed…

We propose a unified model for jet formation applicable to active galactic nuclei, young stellar objects, and X-ray binaries. In this model, the binding energy released from the accretion disk is primarily stored as turbulence rather than…

Classical diffusion models have shown superior generative results. Exploring them in the quantum domain can advance the field of quantum generative learning. This work introduces Quantum Generative Diffusion Model (QGDM) as their simple and…

Introducing a reduced particle stiffness in discrete element method (DEM) allows for bigger time steps and therefore fewer total iterations in a simulation. Although this approach works well for dry non-adhesive particles, it has been shown…

Prompt learning has demonstrated promising results in fine-tuning pre-trained multimodal models. However, the performance improvement is limited when applied to more complex and fine-grained tasks. The reason is that most existing methods…

This paper proposes DiffPF, a differentiable particle filter that leverages diffusion models for state estimation in dynamic systems. Unlike conventional differentiable particle filters, which require importance weighting and typically rely…

Diffusion world models have recently become competitive for online model-based reinforcement learning, but current approaches expose a tension: pixel diffusion is effective but computationally expensive while the latest latent diffusion…

Despite the tremendous success of diffusion generative models in text-to-image generation, replicating this success in the domain of image compression has proven difficult. In this paper, we demonstrate that diffusion can significantly…

The theoretical description of the physics of multi-jets in hadronic collisions at high energies is based on "merging" methods, which combine short-timescale production of jets with long-timescale evolution of partonic showers. We point out…