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The last decade was remarkable for neutrino physics. In particular, the phenomenon of neutrino flavor oscillations has been firmly established by a series of independent measurements. All parameters of the neutrino mixing are now known and…
Intelligent assistive technologies are increasingly recognized as critical daily-use enablers for people with disabilities and age-related functional decline. Longitudinal studies, curation of quality datasets, live monitoring in activities…
The Large Hadron Collider (LHC) at CERN has generated in the last decade an unprecedented volume of data for the High-Energy Physics (HEP) field. Scientific collaborations interested in analysing such data very often require computing power…
This paper presents a new data-driven finite element framework that is applicable to a broad range of engineering simulation problems. In the data-driven approach, the conservation laws and boundary conditions are satisfied by means of the…
Computing has become a major component of all particle physics experiments and in many areas of theoretical particle physics. Progress in HEP experiment and theory will require significantly more computing, software development, storage,…
Modern ML methods excel when training data is IID, large-scale, and well labeled. Learning in less ideal conditions remains an open challenge. The sub-fields of few-shot, continual, transfer, and representation learning have made…
The road map to the FCC Feasibility Study Report, for submission to the next Update of the European Strategy for Particle Physics, will require detailed simulation and advanced reconstruction algorithms to explore and maximise the physics…
A directed R&D program is presently underway in the U.S. to evaluate the designs and technologies required to provide muon-based high energy physics (HEP) accelerator capabilities. Such capabilities have the potential to provide unique…
The finite element method (FEM) is among the most commonly used numerical methods for solving engineering problems. Due to its computational cost, various ideas have been introduced to reduce computation times, such as domain decomposition,…
Deep learning has revolutionised artificial intelligence (AI) by enabling automatic feature extraction and function approximation from raw data. However, it faces challenges such as a lack of out-of-distribution generalisation, catastrophic…
Modern-day accelerator neutrino facilities are excellent venues for searches for new-physics particles. Many distinct new-physics models predict overlapping signatures and phenomenology in these experiments. In this work, we advocate for…
The intersection of physics and machine learning has given rise to the physics-enhanced machine learning (PEML) paradigm, aiming to improve the capabilities and reduce the individual shortcomings of data- or physics-only methods. In this…
Providing fast and accurate solutions to partial differential equations is a problem of continuous interest to the fields of applied mathematics and physics. With the recent advances in machine learning, the adoption learning techniques in…
Across many domains, large swaths of digital assets are being stored across distributed data repositories, e.g., the DANDI Archive [8]. The distribution and diversity of these repositories impede researchers from formally defining…
Every year the PHENIX collaboration deals with increasing volume of data (now about 1/4 PB/year). Apparently the more data the more questions how to process all the data in most efficient way. In recent past many developments in HEP…
There exists one experimental result that cannot be explained by the Standard Model (SM), the current theoretical framework for particle physics: non-zero masses for the neutrinos (elementary particles travelling close to light speed,…
Recent advances in scientific machine learning (SciML) have enabled neural operators (NOs) to serve as powerful surrogates for modeling the dynamic evolution of physical systems governed by partial differential equations (PDEs). While…
Recently, computational modeling has shifted towards the use of deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and real-time control by…
With the growing complexity of computational and experimental facilities, many scientific researchers are turning to machine learning (ML) techniques to analyze large scale ensemble data. With complexities such as multi-component workflows,…
Physics-Informed Neural Networks (PINNs) have gained popularity in solving nonlinear partial differential equations (PDEs) via integrating physical laws into the training of neural networks, making them superior in many scientific and…