Results 101 to 110 of about 6,391,262 (321)

Orbital anomaly reconstruction using deep symbolic regression [PDF]

, 2020
This work explores the combination of Sparse and Symbolic Regression, here called Deep Symbolic Regression, for the autonomous reconstruction of orbital anomalies.
Vasile, Massimiliano, Manzi, Matteo
core   +2 more sources

Physics‐Grounded Materials Artificial Intelligence for Reliable Materials Discovery

Advanced Functional Materials, EarlyView.
Physics‐Grounded Materials AI (PhysMat AI) integrates physical priors, descriptors, constraints, verification, and data infrastructure into a unified full‐stack framework, enabling reliable, interpretable, and autonomous AI‐driven materials discovery.
Yuhang Wang, Qian Wang, Seong‐Hoon Jang, Hao Li   +3 more
wiley   +1 more source

Population Dynamics in Genetic Programming for Dynamic Symbolic Regression

Applied Sciences
This paper investigates the application of genetic programming (GP) for dynamic symbolic regression (SR), addressing the challenge of adapting machine learning models to evolving data in practical applications.
Philipp Fleck, Bernhard Werth, Michael Affenzeller   +2 more
doaj   +1 more source

Autonomous Multi‐Objective Nanoscale Characterization of Combinatorial (Al, Sc, B)N Films Reveals Composition‐Dependent Ferroelectric Regimes

Advanced Functional Materials, EarlyView.
Autonomous scanning probe microscopy and multi‐objective Bayesian optimization navigate a ternary (Al,Sc,B)N combinatorial library. Registered photoluminescence, electron‐probe compositional mapping, and X‐ray diffraction connect local electromechanical function to defect‐sensitive emission, composition, and crystal structure.
Yu Liu, Aditya Raghavan, Utkarsh Pratiush, Ian Mercer, Pochun Hsieh, Albert Suceava, Chih‐Yu Lee, Edgar Dimitrov, Mauricio Terrones, Ichiro Takeuchi, Venkatraman Gopalan, Jon‐Paul Maria, Sergei V. Kalinin   +12 more
wiley   +1 more source

Symbolic Regression for the Determination of Joint Roughness Coefficient

Mathematical and Computational Applications
In this study, a novel symbolic regression-based empirical equation has been developed to compute the joint roughness coefficient (JRC) value based on the statistical parameters of rock joints.
Yuyang Zhao, Hongbo Zhao
doaj   +1 more source

Diffusion-Based Symbolic Regression

CoRR
Diffusion has emerged as a powerful framework for generative modeling, achieving remarkable success in applications such as image and audio synthesis. Enlightened by this progress, we propose a novel diffusion-based approach for symbolic regression. We construct a random mask-based diffusion and denoising process to generate diverse and high-quality ...
Zachary Bastiani, Robert M. Kirby, Jacob D. Hochhalter, Shandian Zhe   +3 more
openaire   +3 more sources

Self-composition by Symbolic Execution [PDF]

, 2013
This work is licensed under a CC-BY Creative Commons Attribution 3.0 Unported license (http://creativecommons.org/licenses/by/3.0/)urn: urn:nbn:de:0030-drops-42770urn: urn:nbn:de:0030-drops-42770Self-composition is a logical formulation of non ...
Phan, Q-S, Phan, Quoc-Sang
core   +1 more source

Engineered Microvascular Model of the Blood–Brain–Tumor Barrier Reveals Endothelial Remodeling in Diffuse Midline Glioma

Advanced Healthcare Materials, EarlyView.
We developed a patient‐derived, functional microfluidic model of the diffuse midline glioma (DMG) blood–brain–tumor barrier (BBTB) comprised of endothelial cells, astrocytes, pericytes, and tumor cells. The system forms perfusable microvasculature, reveals the BBTB retains vascular integrity, identifies DMG‐specific transcriptomic changes distinct from
Kimberly R. Bennett, Yann S. Vanrobaeys, Tessy Teboh, Zakiya Smith, Anderson Scott, Cynthia Hajal, Paula T. Hammond, Joelle P. Straehla   +7 more
wiley   +1 more source

Bias and Variance Analysis of Contemporary Symbolic Regression Methods

Applied Sciences
Symbolic regression is commonly used in domains where both high accuracy and interpretability of models is required. While symbolic regression is capable to produce highly accurate models, small changes in the training data might cause highly dissimilar ...
Lukas Kammerer, Gabriel Kronberger, Stephan Winkler   +2 more
doaj   +1 more source

Beyond Presumptions: Toward Mechanistic Clarity in Metal‐Free Carbon Catalysts for Electrochemical H2O2 Production via Data Science

Advanced Materials, EarlyView.
Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu, Hsi‐wen Wu, Xiao Wang, Jie Zhang   +3 more
wiley   +1 more source