Proposes a zero-copy architecture that eliminates the CPU/GPU data transfer bottleneck in Physics-AI workloads by leveraging Apple Silicons unified memory. Describes a pipeline where particle simulation data (OpenFPM/Metal) resides in shared memory that MLX neural networks can read in-place, enabling real-time physics-ML feedback loops impossible on discrete GPU architectures.
Nigeria's oil and gas pipeline network spanning over 5,000 km of trunk lines and more than 3,000 km of flow lines loses an estimated one billion US dollars annually to pipeline failures, environmental incidents, and non-productive time. The dominant monitoring approach in operati…
Physics-Informed Neural Networks (PINNs) offer a promising bridge between deep learning and biophysical modeling by embedding differential equations directly into the learning process. This paper explores an automated framework using Bayesian Optimization (BO) and PINNs in order…
This deposit contains the field data, synthetic training datasets, trained network weights and analysis code supporting the article "Physics-informed neural network inversion of electrical resistivity tomography data: amortized optimization with field validation in the Moroccan M…
Google Colab and Python code for generating an ETDRK4 numerical reference, training matched laboratory-frame and co-moving-frame physics-informed neural networks, applying PDE-dominant and conservation-aware optimization stages, computing diagnostics, and reproducing the main and…
Physics-informed neural networks (PINNs) are promoted as a general differential-equation solver, but the accuracy actually achieved varies by orders of magnitude across problem types, and that variation is rarely laid out in one place. This report is a method atlas: a runnable ca…