arXiv Machine Learning By Fernando Galaz Prieto, Antti Lassila, Maryam Samavaki, Sampsa Pursiainen

In Silico Study for Optimizing Intensity and Focality Electrode Configurations for Directional DBS Under Uncertainty Using Metaheuristic L1L1 Method

Read the original on arXiv Machine Learning →

arXiv:2506. 13452v3 Announce Type: replace-cross Abstract: Background and Objective: As Deep Brain Stimulation (DBS) advances toward directional leads and optimization-based current steering, selecting electrode contact configurations becomes complex.

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arXiv AI
Sep 24

AI-Driven Neural Surrogates for In Silico Design of Cognitive-Affective Neuromodulation Targets

arXiv:2609.27729v1 Announce Type: cross Abstract: In neuropsychiatry, the primary goal is often not only to decode brain activity but to change it, for example to lessen a negative affective bias or...

By Marco Rothermel, Madleen Stenger, Soroush Daftarian, Svenja Jule Francke, Bita Shariatpanahi, Jos\'e C. Garc\'ia Alanis, Mohammad-Ali Nikouei Mahani, Stefan G. Hofmann, Tim Hahn, Hamidreza Jamalabadi
arXiv AI
Jul 8

Reduced NEXI protocol for the quantification of human gray matter microstructure on the Connectome 2.0 scanner

arXiv:2509. 09513v3 Announce Type: replace-cross Abstract: Biophysical diffusion MRI models like Neurite Exchange Imaging (NEXI) are essential for probing gray matter microstructure, estimating compartment diffusivities, neurite fraction, and exchange time.

By Quentin Uhl, Tommaso Pavan, Julianna Gerold, Kwok-Shing Chan, Yohan Jun, Shohei Fujita, Aneri Bhatt, Yixin Ma, Qiaochu Wang, Hong-Hsi Lee, Susie Y. Huang, Berkin Bilgic, Ileana Jelescu
arXiv Machine Learning
Aug 20

A FEM-Based Surrogate Modelling and Optimization Framework for Physics-Constrained Electromagnetic Coil Design

The paper presents a surrogate‑assisted optimization framework for designing a seven‑parameter current‑excited electromagnetic coil, coupling a 2‑D axisymmetric FEM model with a Matern 5/2 Gaussian‑process surrogate. Sequential Bayesian optimization using expected improvement (EI) is compared with COBYLA and BOBYQA, showing that the ranking of methods depends on the FEM evaluation budget and that different methods excel at early progress, terminal response, or computational cost. A retrospective study indicates no clear advantage of EI over posterior‑mean ranking on this smooth response surface, and the results are specific to the axisymmetric benchmark used.

By Yucheng Liu
arXiv Machine Learning
Jun 3

Correcting Neural Operator Spectral Bias via Diffusion Posterior Sampling with Sparse Observations

arXiv:2606. 03936v1 Announce Type: new Abstract: Neural operator surrogates (NO) approximate PDE solutions orders of magnitude faster than numerical solvers, but suffer from spectral bias: high-frequency content is systematically attenuated, limiting reliability where fine-scale structure matters.

By Niccol\`o Perrone, Fanny Lehmann, Stefania Fresca, Filippo Gatti