ReservoirNeuralBench – A controlled benchmark of neural surrogates for 3D reservoir simulation on the Norne field.
Open-source engineering workbench combining numerical simulation, Physics ML, and accelerated scientific computing.
This repository contains Jupyter notebooks from my learning journey with Physics-Informed Neural Networks (PINNs). These are not complete projects but serve as educational resources to explore core and intermediate concepts in applying neural networks to solve partial differential equations (PDEs).
Fine-tuned DeepCFD UNet surrogate model for CFD flow field prediction
Physics-aware machine learning study for event classification in high-energy physics.
Predict steady-state 2D laminar flow fields in milliseconds using a deep learning surrogate model for Computational Fluid Dynamics.
Ensemble uncertainty for a stellarator surrogate. Where predictions on the ConStellaration dataset can be trusted, and when to defer to the physics solver.
FNO-RC: Fourier Neural Operator with Conformal Residual Coupling for PDEs
Active Learning–Guided Adaptive ESR Acquisition | Poster presented at ICESR 2026, IISc Bengaluru
Frontier Python SDK: offline-first experiment tracking for physics ML, the native Rust core, and the NVIDIA PhysicsNeMo bridge.
NVIDIA PhysicsNeMo plugin for DimensionLab Frontier: logging, model registry, checkpoint compatibility and dataset adapters. Installable independently of the platform.
Does an energy-trained transformer encode entanglement structure it was never trained on? Pre-registered study with exact-solvable ground truth. Apparatus and instrument validated; measurement deliberately not yet made.
Frontier for Zed: JSON schemas, snippets and the Frontier MCP context server for physics-ML training configs, RunSpecs, data-prep recipes, pipelines, hub entries and plugin manifests. A Zed extension (extension.toml plus a small wasm32-wasip2 crate), installable as a dev extension today and submitted to the Zed extension registry later.
Curated hub of public physics-AI datasets and benchmarks: catalog, fetchers and converters to the Frontier dataset format.
Physics-guided ML for tool wear classification from machining vibration signals using Random Forest and FFT features
σFlow-PDE: A drop-in H-Bar training engine that escapes the σ-trap in neural PDE solvers via live σ/δ/α ODE integration, autonomous phase curriculum, and auto-falsification.
Mechanistic interpretability of classical neural networks trained on quantum data — using sparse autoencoders to detect quantum structure in classical representations.
Frontier data model and dataset format: the frontier-dataset specification, the plugin contract, and reference readers/writers in Rust and Python.
Connector plugins that turn CFD/CAE solver output (OpenFOAM, VTK, Fluent, STAR-CCM+, CGNS, LS-DYNA, ...) into Frontier datasets. Write your own in an afternoon.
This repository contains a lightweight CNN-based approach for top-quark jet classification, using data from CERN’s public Zenodo dataset.
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