The paper introduces SurgiATM, a lightweight physics-guided module for removing surgical smoke from laparoscopic endoscopic frames. It integrates a physics-based atmospheric model with a data-driven deep learning approach via a Mixture-of-Experts output stage, using a Laplacian-like error distribution to model smoke. SurgiATM adds only two hyperparameters and no extra trainable weights, enabling easy integration into existing desmoking architectures and improving accuracy and stability across multiple datasets and procedures.
By Mingyu Sheng, Jianan Fan, Dongnan Liu, Guoyan Zheng, Ron Kikinis, Weidong Cai
arXiv:2605. 08832v3 Announce Type: replace Abstract: Neural surrogate models for computational fluid dynamics (CFD) are typically trained as forward operators that map explicit problem specifications, such as geometry and boundary conditions, to solution fields.
By Jonas Weidner, Yeray Martin-Ruisanchez, Daniel Rueckert, Benedikt Wiestler, Julian Suk
arXiv:2509. 15900v2 Announce Type: replace-cross Abstract: This work aims to predict blood flow with non-Newtonian viscosity in stenosed arteries using convolutional neural network (CNN) surrogate models.
By Simon Klaes, Axel Klawonn, Natalie Kubicki, Martin Lanser, Kengo Nakajima, Takashi Shimokawabe, Janine Weber
arXiv:2605. 08843v2 Announce Type: replace Abstract: Neural surrogate models for physical simulations are trained on discretized samples of continuous domains, where the induced empirical measure leads to uneven supervision, biasing optimization and causing spatial inconsistencies in physical fidelity.
By Yuan Mei, Xingyu Song, Xiaowen Song, Naoya Takeishi
arXiv:2606. 09949v1 Announce Type: cross Abstract: Data-driven PDE surrogates are trained with data produced by numerical PDE solvers.
By Pierre Cesar (DATAMOVE), Sofya Dymchenko (DATAMOVE), Abhishek Purandare (DATAMOVE), Bruno Raffin (DATAMOVE)
arXiv:2608. 00852v1 Announce Type: new Abstract: Understanding and controlling complex dynamical systems often requires executing thousands of numerical simulations across vast parametric landscapes, which is time-consuming.
By Ajitesh Srivastava