Results 51 to 60 of about 4,469 (193)
Harnessing Machine Learning to Understand and Design Disordered Solids
Advanced Intelligent Discovery, EarlyView.This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
MolMiner: Toward Controllable, Three‐Dimensional‐Aware, Fragment‐Based Molecular Design
Advanced Intelligent Discovery, EarlyView.MolMiner is a fragment‐based, geometry‐aware, and order‐agnostic generative model for molecular design with strong inductive biases. Using symmetry‐aware fragment assembly, dynamic three‐dimensional geometry, and multi‐property conditioning, MolMiner enables interpretable and controllable molecular generation.
Raul Ortega‐Ochoa +2 more
wiley +1 more source
Wasserstein Distance on Configuration Space [PDF]
Potential Analysis, 2008 We investigate here the optimal transportation problem on configuration space for the quadratic cost. It is shown that, as usual, provided that the corresponding Wasserstein is finite, there exists one unique optimal measure and that this measure is supported by the graph of the derivative (in the sense of the Malliavin calculus) of a ``concave'' (in a
openaire +3 more sources
2D Implementation of Kinetic‐Diffusion Monte Carlo in Eiron
Contributions to Plasma Physics, EarlyView.ABSTRACT Particle‐based kinetic Monte Carlo simulations of neutral particles are one of the major computational bottlenecks in tokamak scrape‐off layer simulations. This computational cost comes from the need to resolve individual collision events in high‐collisional regimes.
Oskar Lappi +3 more
wiley +1 more source
Explainable AI Using the Wasserstein Distance
IEEE AccessAI-based decision systems often lack transparency due to their black-box nature and lack explanations of their decisions, which are crucial for life-changing applications such as disease diagnosis, financial investments, and military decisions ...
Shion Samadder Chaudhury +2 more
doaj +1 more source
Deep Underground Science and Engineering, EarlyView.The flowchart illustrates rock specimen testing, vibration signal acquisition, and feature extraction with Gaborlet and sparse filtering for classification. Abstract Traditional lithology identification methods mainly rely on core sampling and well‐logging data.
Jian Hao +5 more
wiley +1 more source
High-Dimensional Distributionally Robust Mean-Variance Efficient Portfolio Selection
Mathematics, 2023 This paper introduces a novel distributionally robust mean-variance portfolio estimator based on the projection robust Wasserstein (PRW) distance. This approach addresses the issue of increasing conservatism of portfolio allocation strategies due to high-
Zhonghui Zhang, Huarui Jing, Chihwa Kao
doaj +1 more source
Bounding Wasserstein Distance with Couplings
Journal of the American Statistical Association52 pages, 8 ...
Niloy Biswas, Lester Mackey
openaire +2 more sources
Generative Models in Inorganic Crystals Discovery and Inverse Design
ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.Generative inverse‐design samples from the vast inorganic crystal design space by starting from target properties such as band gap, stability, and ion transport. This Review examines the representations, generative models, and validation workflows needed to translate candidate structures into stable, potentially synthesizable materials for applications
Tao Li +5 more
wiley +1 more source
Remote SensingFull-waveform inversion (FWI) is one of the most promising techniques in current ground-penetrating radar (GPR) inversion methods. The least-squares method is usually used, minimizing the mismatch between the observed signal and the simulated signal ...
Kai Lu +4 more
doaj +1 more source