Results 201 to 210 of about 49,829 (265)

Generative and Experimental Validation of High Refractive Index Polymers via Domain Knowledge Approach with Small Data

Advanced Intelligent Discovery, EarlyView.
This research demonstrates that the combination of domain knowledge–based multiple regression, multi‐objective Bayesian optimization, and generative models is a suitable prediction tool for candidates of high refractive index polymers, even with the constraints in the model trained on limited data. The experimental validation can reproduce the proposed
Takuya Yokoo, Seigo Watanabe, Hana Matsunaga, Kenichi Oyaizu   +3 more
wiley   +1 more source

Estimation of Detailed Cardiac Doses for Pediatric Radiation Therapy Patients in National Wilms Tumor Study. [PDF]

Int J Radiat Oncol Biol Phys
Lee C, Mille MM, Griffin KT, Rigsby C, Popescu A, Gopalakrishnan M, Leisenring W, Peterson S, Dome JS, Laurie F, Fitzgerald TJ, Bentzen S, Jung JW, Lee C, Kalapurakal JA.   +14 more
europepmc   +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, Tejs Vegge, Jes Frellsen   +2 more
wiley   +1 more source

Distance-adaptive geometric margins for residual rotational uncertainty in single-isocenter multitarget stereotactic radiosurgery. [PDF]

Phys Imaging Radiat Oncol
Deng J, Piao Z, Song X, Li D, Hu J, Ding S, Liu Y, Chen L, Zhou G, Sun Y, Huang X, Wang G.   +11 more
europepmc   +1 more source

Predicting Performance of Hall Effect Ion Source Using Machine Learning

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park, Guentae Doh, Dongho Lee, Youngho Kim, Changmin Shin, Su‐Jin Shin, Young‐Chul Ghim, Sanghoo Park, Wonho Choe   +8 more
wiley   +1 more source

Generation of 3D reference dosimetric datasets towards adoption of model-based dose calculations for permanent implant prostate brachytherapy. [PDF]

Med Phys
Akbari F, Peppa V, Ouellet S, Moghadam N, Oliver S, Gimenez-Alventosa V, Beaulieu L, Vijande J, Thomson RM.   +8 more
europepmc   +1 more source

Compact Modeling of Volatile‐Switching Electrochemical Metallization Memory Cells by Means of the Electromotive Force

Advanced Intelligent Systems, EarlyView.
A volatile‐switching compact model of electrochemical metallization memory cells for neuromorphic architecture is developed and validated by reliable reproduction of device characterization measurements: I−V sweeps, SET kinetics, relaxation dynamics.
Rana Walied Ahmad, Solomon Amsalu Chekol, Julius Rasbach, Susanne Hoffmann‐Eifert, Stephan Menzel   +4 more
wiley   +1 more source

Comparative evaluation of the Mayo Clinic Florida microdosimetric kinetic model and mMKM for carbon ion treatment planning: A matRad-based analysis. [PDF]

J Appl Clin Med Phys
Hartzell S, Yaddanapudi S, Furutani KM, Parisi A, Liang X, Park C, Lu B, Tan J, Seckler L, Cristoforetti R, Inaniwa T, Wahl N, Beltran CJ.   +12 more
europepmc   +1 more source

Overcoming the Phase Discontinuity Problem in RIS Optimization: A Robust and Scalable Phase‐Aware Deep Regression Framework via Hybrid CNN–LSTM Architecture

Advanced Intelligent Systems, EarlyView.
The phase discontinuity problem—where the cyclic nature of phase angles causes catastrophic errors near the ±π boundary—is a fundamental obstacle in learning‐based reconfigurable intelligent surface (RIS) optimization. A phase‐aware hybrid CNN–LSTM framework resolves this by decomposing phase predictions into sine–cosine components, mapping circular ...
Seda Savaşçı Şen, Ali Çalhan, Murtaza Cicioğlu, Oğuzhan Demiryürek   +3 more
wiley   +1 more source

Neural Quantum States Based on Selected Configurations. [PDF]

J Phys Chem Lett
Julian Solanki M, Ding L, Reiher M.
europepmc   +1 more source