arXiv Machine Learning June 5, 2026 By Shah Pallav Dhanendrakumar, Saikat Pal, Sitikantha Roy

Integrating Mechanistic and Data-Driven Models for Neurological Disorders through Differentiable Programming

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arXiv:2606. 06094v1 Announce Type: cross Abstract: Advances in computational modeling, neuroimaging, and artificial intelligence are revolutionizing the modeling of neurological disorders for improved diagnostics, prognosis, and treatment planning.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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arXiv Machine Learning

Jun 16

HAPI-EP: Towards Hybrid, Adaptive, and Predictive Digital Twins of Cardiac Electrophysiology

arXiv:2606. 15637v1 Announce Type: new Abstract: A digital twin (DT) of a patient-specific heart offers significant potential in personalized medicine.

By Sumeet Vadhavkar, Xiajun Jiang, Yubo Ye, Maryam Toloubidokhti, Linwei Wang

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arXiv Machine Learning

Jul 17

RTS Smoother-Guided Learning of Physics-Based Neural Differential Models

arXiv:2607. 15180v1 Announce Type: new Abstract: Ordinary differential equations (ODEs) are widely used to model dynamical systems in physics, biology, neuroscience, and physiology, but in many applications some equations of the dynamics are unknown and only a subset of the state variables are measured.

By Ahmet Demirkaya, Georgios Stratis, Tales Imbiriba, Zachary D. Danziger, Deniz Erdogmus

benchmarks

arXiv Machine Learning

Jun 19

Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System

arXiv:2606. 19754v1 Announce Type: new Abstract: Partial differential equations (PDEs) play a central role in modeling complex physical, biological, and engineering systems.

By Zhiwen Yu, Derong Yang, Liujian Zhang, Kaixiang Yang, Peilin Zhan, Jianmin Lv, Jane You, C. L. Philip Chen

arXiv Machine Learning

Jul 9

Reliable mechanistic operator recovery with biologically-informed neural networks: principles for architecture and optimisation design

arXiv:2607. 07425v1 Announce Type: cross Abstract: Many biological processes are governed by complex dynamical mechanisms that remain incompletely understood despite increasing volumes of experimental data.

By Rebecca M. Crossley, Yuan Yin, Sarah L. Waters, Ruth E. Baker

rag diffusion benchmarks

arXiv Machine Learning

Aug 5

A Physics-Flavored Transformer Network for Parametrizing Contraction Dynamics of Engineered Skeletal Muscle Tissues

arXiv:2608. 03927v1 Announce Type: new Abstract: Engineered Skeletal Muscle Tissues (ESMs) have become a key structure for biomedical disease modeling and pharmacological screening, yet their functional characterization often relies on simplistic metrics like peak force, discarding critical kinetic information.

By Mattias Luber, Timo Betz

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arXiv Machine Learning

Jun 2

Hybrid Neural Ordinary Differential Equations for Data-Efficient Polymerization Modeling with Incomplete Kinetics

arXiv:2606. 02145v1 Announce Type: new Abstract: Accurate prediction of polymerization dynamics is essential for process design, control, and optimization.

By Marah Almanasreh, Alexander Mitsos, Eike Cramer

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HAPI-EP: Towards Hybrid, Adaptive, and Predictive Digital Twins of Cardiac Electrophysiology

RTS Smoother-Guided Learning of Physics-Based Neural Differential Models

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