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As the most important solvent, water has been at the center of interest since the advent of computer simulations. While early molecular dynamics and Monte Carlo simulations had to make use of simple model potentials to describe the atomic…

Soft Condensed Matter · Physics 2024-02-01 Amir Omranpour , Pablo Montero De Hijes , Jörg Behler , Christoph Dellago

Artificial intelligence (AI) provides many opportunities to improve private and public life. Discovering patterns and structures in large troves of data in an automated manner is a core component of data science, and currently drives…

Machine Learning · Computer Science 2020-09-25 Vaishak Belle , Ioannis Papantonis

Current hydrological modeling methods combine data-driven Machine Learning (ML) algorithms and traditional physics-based models to address their respective limitations incorrect parameter estimates from rigid physics-based models and the…

Machine Learning · Computer Science 2024-02-22 Mostafa Esmaeilzadeh , Melika Amirzadeh

As ultracold atom experiments become highly controlled and scalable quantum simulators, they require sophisticated control over high-dimensional parameter spaces and generate increasingly complex measurement data that need to be analyzed…

Quantum Gases · Physics 2025-09-11 Henning Schlömer , Annabelle Bohrdt

The field of machine learning (ML) has gained widespread adoption, leading to significant demand for adapting ML to specific scenarios, which is yet expensive and non-trivial. The predominant approaches towards the automation of solving ML…

Machine Learning · Computer Science 2024-02-20 Lei Zhang , Yuge Zhang , Kan Ren , Dongsheng Li , Yuqing Yang

Scientific progress is tightly coupled to the emergence of new research tools. Today, machine learning (ML)-especially deep learning (DL)-has become a transformative instrument for quantum science and technology. Owing to the intrinsic…

Quantum Physics · Physics 2025-08-15 Timothy Heightman , Marcin Płodzień

Conventional machine learning studies generally assume close-environment scenarios where important factors of the learning process hold invariant. With the great success of machine learning, nowadays, more and more practical tasks,…

Machine Learning · Computer Science 2022-08-10 Zhi-Hua Zhou

In recent years, the use of Machine Learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditional electronic-structure methods. One of the most promising…

Automated machine learning techniques benefited from tremendous research progress in recently. These developments and the continuous-growing demand for machine learning experts led to the development of numerous AutoML tools. However, these…

Machine Learning · Computer Science 2021-06-15 Alexandru-Ionut Imbrea

The recently increased complexity of Machine Learning (ML) methods, led to the necessity to lighten both the research and industry development processes. ML pipelines have become an essential tool for experts of many domains, data…

Software Engineering · Computer Science 2022-07-18 Giordano d'Aloisio , Antinisca Di Marco , Giovanni Stilo

Machine Learning (ML) is an application of Artificial Intelligence (AI) that uses big data to produce complex predictions and decision-making systems, which would be challenging to obtain otherwise. To ensure the success of ML-enabled…

Software Engineering · Computer Science 2021-09-03 Khan Mohammad Habibullah , Jennifer Horkoff

Despite strong results on many tasks, multimodal large language models (MLLMs) still underperform on visual mathematical problem solving, especially in reliably perceiving and interpreting diagrams. Inspired by human problem-solving, we…

Computer Vision and Pattern Recognition · Computer Science 2026-04-21 Shuhang Chen , Hangjie Yuan , Yunqiu Xu , Pengwei Liu , Tao Feng , Jun Cen , Zeying Huang , Yi Yang

In recent years, machine learning (ML) techniques have emerged as powerful tools for studying many-body complex systems, and encompassing phase transitions in various domains of physics. This mini review provides a concise yet comprehensive…

Nuclear Theory · Physics 2024-01-05 Yu-Gang Ma , Long-Gang Pang , Rui Wang , Kai Zhou

Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains a significant obstacle, in large part due to the radically…

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc.…

Networking and Internet Architecture · Computer Science 2018-12-04 Francesco Musumeci , Cristina Rottondi , Avishek Nag , Irene Macaluso , Darko Zibar , Marco Ruffini , Massimo Tornatore

Streamflow prediction is one of the key challenges in the field of hydrology due to the complex interplay between multiple non-linear physical mechanisms behind streamflow generation. While physics based models are rooted in rich…

Atmospheric and Oceanic Physics · Physics 2025-11-12 Ankush Khandelwal , Shaoming Xu , Xiang Li , Xiaowei Jia , Michael Stienbach , Christopher Duffy , John Nieber , Vipin Kumar

Software organizations are increasingly incorporating machine learning (ML) into their product offerings, driving a need for new data management tools. Many of these tools facilitate the initial development of ML applications, but…

Software Engineering · Computer Science 2022-07-19 Shreya Shankar , Aditya Parameswaran

Machine learning (ML) is a rapidly growing area of research in the field of particle physics, with a vast array of applications at the CERN LHC. ML has changed the way particle physicists conduct searches and measurements as a versatile…

High Energy Physics - Experiment · Physics 2024-10-01 Javier M. Duarte

Unstructured grid data are essential for modelling complex geometries and dynamics in computational physics. Yet, their inherent irregularity presents significant challenges for conventional machine learning (ML) techniques. This paper…

Combining machine learning (ML) with computational fluid dynamics (CFD) opens many possibilities for improving simulations of technical and natural systems. However, CFD+ML algorithms require exchange of data, synchronization, and…

Machine Learning · Computer Science 2024-06-25 Tomislav Maric , Mohammed Elwardi Fadeli , Alessandro Rigazzi , Andrew Shao , Andre Weiner