On the Impact of Gravity Compensation on Reinforcement Learning in Goal-Reaching Tasks for Robotic Manipulators by Jonathan Fugal, Jihye Bae, Hasan A. Poonawala

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Exploring Variational Methods in Hydro- and Hemodynamics Through Incompressible Velocity Distribution Sampling by Ivan Stebakov, Alexei Kornaev, Elena Kornaeva, Sergey Majorov

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Artificial Neural Networks as a Natural Tool in Solution of Variational Problems in Hydrodynamics by Ivan Stebakov, Alexei Kornaev, Elena Kornaeva, Nikita Litvinenko, Yuri Kazakov, Oleg Ivanov, Bulat Ibragimov

Subjects: “…Physics-based machine learning…”
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Using physics to extend the range of machine learning models for an aerodynamic, hydraulic and combusting system: The toy model concept by Indranil Brahma, Robert Jennings, Bradley Freid

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A review of physics-based machine learning in civil engineering by Shashank Reddy Vadyala, Sai Nethra Betgeri, John C. Matthews, Elizabeth Matthews

Subjects: “…Physics-based machine learning…”
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Slip Tendency Analysis From Sparse Stress and Satellite Data Using Physics‐Guided Deep Neural Networks by Thomas Poulet, Pouria Behnoudfar

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Editorial: Nanofluidics: computational methods and applications by Filippos Sofos, Konstantinos Ritos, Konstantinos Ritos, Aggelos Avramopoulos

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POD-Galerkin reduced order models and physics-informed neural networks for solving inverse problems for the Navier–Stokes equations by Saddam Hijazi, Melina Freitag, Niels Landwehr

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A switching Gaussian process latent force model for the identification of mechanical systems with a discontinuous nonlinearity by Luca Marino, Alice Cicirello

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Physics-based surrogate model for reinforced concrete corrosion simulation by Syifaul Huzni, Israr B.M. Ibrahim, Syarizal Fonna, Ramana Pidaparti

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Current state-of-the-art and utilities of machine learning for detection, monitoring, growth prediction, rupture risk assessment, and post-surgical management of abdominal aortic a... by Seungik Baek, Amirhossein Arzani

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A Deep Learning Approach to Predict Abdominal Aortic Aneurysm Expansion Using Longitudinal Data by Zhenxiang Jiang, Huan N. Do, Jongeun Choi, Whal Lee, Seungik Baek

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Predicting meltpool depth and primary dendritic arm spacing in laser powder bed fusion additive manufacturing using physics-based machine learning by Alex R. Riensche, Benjamin D. Bevans, Grant King, Ajay Krishnan, Kevin D. Cole, Prahalada Rao

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