arXiv Machine Learning August 18, 2026 By Xinling Yu, Yixing Li, Ziyue Liu, Xin Ai, Zhiyu Zeng, Hai Li, Zheng Zhang

DeepOHeat-v2: Self-Improving Operator Learning for Fast and Trustworthy Thermal Optimization in 3D-IC Design

Read the original on arXiv Machine Learning →

arXiv:2608. 16080v1 Announce Type: new Abstract: Thermal-aware optimization of multi-die 3D integrated circuits evaluates many designs, each a costly heat-equation solve.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

benchmarks

arXiv AI

Sep 1

SS-ESOAP: Self-Scaled Adaptive Preconditioning for Physics-Informed Learning

arXiv:2608.29448v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) often face ill-conditioned objectives that limit high-accuracy training. Dense quasi-Newton methods improve...

By Guangyuan Wang, Mads Toftrup, Sebastian Loeschcke, Yixuan Wang, Anima Anandkumar

benchmarks

arXiv AI

Aug 11

Self-Attention to Operator Learning-based 3D-IC Thermal Simulation

arXiv:2510. 15968v2 Announce Type: replace-cross Abstract: Thermal management in 3D ICs is increasingly challenging due to higher power densities.

By Zhen Huang, Hong Wang, Wenkai Yang, Muxi Tang, Depeng Xie, Ting-Jung Lin, Yu Zhang, Wei W. Xing, Lei He

fine-tuning benchmarks

arXiv Machine Learning

3d ago

Probe-Space Preconditioning for Fast and Stable Zero-Order Training

arXiv:2609.38095v1 Announce Type: new Abstract: Backpropagation (BP) dominates deep learning but imposes a massive memory tax. For example, training OPT-30B with Adam requires $\approx$ 600GB of GPU...

By Francois Chaubard, Mykel J. Kochenderfer, Chris R\'e

benchmarks

arXiv Machine Learning

Sep 4

Mesh-Native Physics-Informed Graph Surrogates for TCAD-in-the-Loop Design Space Exploration

The paper introduces a physics‑informed graph attention network that directly operates on the tetrahedral mesh used in TCAD simulations of FinFET devices. By predicting electrostatic potential and quasi‑Fermi levels at every mesh node and training with both data loss and finite‑volume current‑continuity residuals, the surrogate retains the underlying carrier‑transport physics while achieving size generalization. Benchmarks against Sentaurus Device show sub‑volt RMSE for the drift‑diffusion fields and a per‑design throughput that is orders of magnitude faster, enabling rapid Pareto‑front exploration of large multi‑fin arrays that would otherwise be prohibitively slow to simulate.

By Leonid Popryho, Ayoub Sadeghi, Inna Partin-Vaisband

rag diffusion efficiency

arXiv AI

Aug 6

MESH: Memory-Efficient Sinkhorn Optimization for Mixture-of-Experts Training

arXiv:2608. 04407v1 Announce Type: cross Abstract: Memory-efficient matrix optimizers such as Sinkhorn gradient descent remove most AdamW optimizer state for dense Transformer matrices, but direct application to Mixture-of-Experts (MoE) training is unreliable.

By Masato Fujitake

llms

arXiv Machine Learning

Jun 25

Silent Failures in Physics-Informed Neural Networks: Parameter Poisoning and the Limits of Loss-Based Validation

arXiv:2606. 25151v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) embed governing equations in their loss function, enabling mesh-free solutions to partial differential equations.

By David McShannon, Nicholas Dietrich

diffusion safety

One email a morning, machine-written

Related stories

SS-ESOAP: Self-Scaled Adaptive Preconditioning for Physics-Informed Learning

Self-Attention to Operator Learning-based 3D-IC Thermal Simulation

Probe-Space Preconditioning for Fast and Stable Zero-Order Training

Mesh-Native Physics-Informed Graph Surrogates for TCAD-in-the-Loop Design Space Exploration

MESH: Memory-Efficient Sinkhorn Optimization for Mixture-of-Experts Training

Silent Failures in Physics-Informed Neural Networks: Parameter Poisoning and the Limits of Loss-Based Validation