arXiv:2607. 05271v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) encounter ill-posed optimization, loss competition, and parameter compensation in partial differential equation (PDE) inverse problems.
By Qian Hu, Bin Fan, Yao Xiao, Zhicheng Lin, Meixin Xiong
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: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
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:2606. 02044v1 Announce Type: new Abstract: Diffusion MRI enables non-invasive probing of tissue microstructure, but accurate parameter estimation is challenged by noise-related effects.
By Bradley G. Karat, Ma\"eliss Jallais, Ali R. Khan, Santiago Aja-Fern\'andez, Jelle Veraart, Marco Palombo
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