Headshot of Allamaprabhu Ani, smiling, brown background

Allamaprabhu Ani

ಅಲ್ಲಮಪ್ರಭು ಅಣಿ

PhD researcher in AI for Science, Computational Mechanics, and Deep Learning, registered at City, St George's, University of London, with teaching and research-facing activity at Queen Mary University of London's School of Engineering and Materials Science.

I am currently wrapping up my PhD and actively seeking full-time industrial R&D roles (Applied Scientist / ML Engineer) starting in late 2026. Reach out if you are building AI for engineering.

About Me

I build fast, scalable, and differentiable solvers for PDEs at the intersection of scientific computing and machine learning. Currently, I am working on finishing my thesis and the phase-field solver paper, alongside a public release of the codebase. I also author a comprehensive tutorial series — from PyTorch training loops to Physics-Informed Neural Networks (PINNs) and Neural Operators.

Before my PhD, I read a lot of Bruhn and Niu while my classmates were learning ANSYS, did an MTech at NIT Silchar, and at 24 founded a small engineering company called Aeroknacks. We shipped automated aerospace structural-analysis tools, reducing 4-hour commercial sizing workflows to just 5 minutes. The bolted-joint tool that paid the bills is now MIT-licensed on GitHub.

In 2025, I spent time at EPFL's Computational Solid Mechanics lab with Prof. Jean-François Molinari, where we published a widely read review of ML for fracture mechanics.

Outside the lab, I've practiced Hindustani classical singing since I was 3. The ragas are familiar; the riyaz is the lifelong part.

Selected Code & Projects

Machine Learning Tutorials

Runnable notebooks and visual explainers for the path I keep using in my own work.

  1. Noisy sine data with train and validation split and a trained MLP fit

    01 intro 45 min

    Machine Learning Training from Scratch: Loss, Gradients, and Overfitting

    Reproduce the classic train-loss-down, validation-loss-up overfitting curve and learn what fixes it.

  2. Pearson correlation heatmap for the UCI auto-MPG regression dataset

    02 intro 40 min

    scikit-learn Regression Tutorial: Explore, Fit, Evaluate, Diagnose

    Build the full tabular ML loop and see why a pretty regression line is not enough.

  3. One-neuron and MLP models fitting a sine wave in PyTorch

    03 intro 55 min

    Neural Networks from One Neuron to a PyTorch MLP Classifier

    See exactly when one neuron fails, why a hidden layer works, and how the same pattern becomes a classifier.

  4. MLP versus CNN training loss and test accuracy on CIFAR-10

    04 intermediate 60 min

    Convolutional Neural Networks from Pixels to Feature Maps

    Train a small CNN, compare it with an MLP, then open the model and inspect the filters.

  5. Data-only neural network, physics-only PINN, and hybrid PINN on a damped oscillator

    05 advanced 70 min

    PINN Tutorial in PyTorch: Damped Oscillator, Autograd, and Inverse Problems

    Use torch.autograd.grad to make a neural network obey an ODE, then turn the same idea into an inverse problem.

Open the full tutorial path →

Recent News & Updates

Papers & Track Record

  1. A. S. Ani, J.-F. Molinari, G. Subhash, S. A. Ponnusami. A matrix-free, differentiable PyTorch solver for phase-field fracture: Formulation, benchmarks, and inverse analysis. arXiv:2606.23458 [cs.CE], 2026. Preprint introducing the matrix-free PyTorch phase-field fracture solver, dynamic and quasi-static benchmarks, and gradient-based inverse recovery of fracture energy.
  2. A. S. Ani, R. Nakka, G. Subhash, J.-F. Molinari, S. A. Ponnusami. Machine learning for computational fracture and damage mechanics: status and perspectives. Engineering Fracture Mechanics, Vol 332, Art 111778, 2026. The field's first comprehensive review; ranked #1 on the journal's most-downloaded list.
  3. A. S. Ani, A. B. Deoghare. Leveraging machine learning for enhanced fatigue life prediction in aluminium alloys. Lecture Notes in Mechanical Engineering, Dec 2024.