Sinter-PiNDiff is a retrainable physics‑integrated neural differentiable framework that predicts density and grain‑size evolution during sintering. It uses two neural networks to learn densification and grain‑growth coefficients within coupled rate equations, and a smooth saturation factor to limit densification near theoretical density. When trained on published data for MgO, Al‑doped ZnO, and CaO‑doped ThO₂, the model achieved the lowest mean errors across twelve material‑metric comparisons compared to multilayer perceptron and residual network baselines, demonstrating the importance of density‑dependent kinetic feedback and providing uncertainty estimates via deep ensembles.
By Zeping Chen, Ani Aprahamian, Khachatur V. Manukyan, Tengfei Luo
arXiv:2604. 14562v2 Announce Type: replace Abstract: Accurate temperature field prediction in metal additive manufacturing (AM) is essential for understanding the process-structure-performance relationship.
By Hyeonsu Lee, Jihoon Jeong
The paper introduces Thermodynamically Consistent Latent Dynamics Identification (tLaSDI) using parametric GENERIC Formalism-Informed Neural Networks (pGFINNs) to predict fracture patterns and evolution in heterogeneous materials. By embedding thermodynamic principles—such as total energy conservation and entropy generation—directly into the neural network architecture, the model operates in a lower‑dimensional latent space and maintains thermodynamic consistency on unseen data. A Geometry‑Conditioned Autoencoder further enhances geometry‑guided learning, and results show that tLaSDI‑pGFINNs outperform a purely data‑driven AE‑LSTM model in accurately capturing fracture behavior.
By Muhamad Alim, Xiaolong He, Mei-Na Tsao, Chi-Hua Yu, J. S. Chen
arXiv:2606. 17235v1 Announce Type: cross Abstract: Machine Learning (ML) models for grain growth prediction are typically trained on idealized synthetic data, yet practical applications require generalization to conditions outside the training distribution.
By Pungponhavoan Tep, Marc Bernacki
NeuralBES is a differentiable emulator that blends a physics‑based resistance–capacitance thermal model with a shared neural encoder to predict building energy use. By mapping building metadata to physically bounded model coefficients, it solves a scalar linear recurrence in log‑space and uses a predictor–corrector loop to handle thermostat nonlinearity while maintaining full‑horizon gradient flow. Trained on the ResStock dataset, NeuralBES accurately models heterogeneous buildings across climate zones, achieving physics‑valid predictions with fewer parameters than transformer and recurrent baselines and outperforming grey‑box RC models in mean absolute percentage error.
By Ting-Yu Dai, Takuya Kurihana, Wing Yee Au, Hon Yung Wong
The paper introduces a Physics Informed Recurrent Neural Network (PIRNN) that simultaneously predicts target time series and unobservable intermediate physical variables, enhancing robustness and interpretability. It adapts to any physical model with multiple equations and variables, demonstrated on groundwater level predictions using the Gardenia model. Experiments on twelve real‑world datasets show PIRNN outperforming several neural network baselines and the Gardenia model, with an ablation study confirming the value of physical knowledge.
By Etienne Lehembre (CA, LIFO), Pascal Audigane (BRGM), Vincent Nguyen (LIFO), Christel Vrain (LIFO, CA), Thi-Bich-Hanh Dao (LIFO, CA)
arXiv:2505. 02979v4 Announce Type: replace-cross Abstract: We propose a novel inverse-modelling approach that estimates the parameters of a simple land-surface model (LSM) by assimilating data into a differentiable, physics-based forward model formulated using convolutional operations.
By Ruiyue Huang, Claire E. Heaney, Maarten van Reeuwijk
arXiv:2603. 28707v3 Announce Type: replace-cross Abstract: We present a physics-based neural network framework for the discovery of constitutive models in fully coupled thermomechanics.
By Hagen Holthusen, Paul Steinmann, Ellen Kuhl
arXiv:2609.15439v1 Announce Type: cross
Abstract: Generative thermodynamic computers turn thermal noise into structured data through Langevin dynamics. We train these systems with a local update at e...
By Huilin Wang, Weibing Deng
arXiv:2608. 07681v1 Announce Type: new Abstract: Accurate and robust time series forecasting is essential in many applications involving physical processes, such as manufacturing monitoring and astrophysical event detection.
By Amal Saadallah, Julia Tjus, Petra Wiederkeher, Wolfgang Rhode
arXiv:2608. 13260v1 Announce Type: new Abstract: Accurate modeling and forecasting of power transformer thermal behavior are critical for reliability, asset lifetime, and optimized power system operation.
By Berk Hadzhamolla, Alexander Johannes Stasik, Signe Riemer-S{\o}rensen
arXiv:2608. 10941v1 Announce Type: new Abstract: Industrial time-series signals, such as turbine temperature and rotational speed in aero-engines, are essential for monitoring the health and operational status of complex dynamical systems.
By Haiteng Wang, Yunfei Zhu, Tao Wang, Yikang Li, Jiabao Dong, Xiaoge Zhang, Lei Ren