Research Direction

I am a PhD candidate in computational mechanics and scientific machine learning, working at the point where numerical methods, high-performance computing, and learning-based models can inform one another. My research focuses on phase-field fracture, differentiable simulation, inverse problems, and the use of machine learning in mechanics. I am registered at City St George's, University of London, where I also contribute as a Graduate Teaching Assistant.

My aim is to make high-fidelity mechanics more useful in practice: faster to run, easier to inspect, and able to work directly with gradients and data. That direction connects fundamental fracture mechanics with engineering software, aerospace structures, and AI for science.

Research and Software

I lead development and maintenance of PhAST, an open, PyTorch-native, GPU-accelerated differentiable solver for phase-field fracture. The work brings explicit dynamics, staggered damage solves, and autograd-compatible inverse analysis into one research codebase. The associated preprint, co-authored with Jean-François Molinari, Ghatu Subhash, and Sathiskumar Anusuya Ponnusami, presents the formulation, benchmarks, and inverse-analysis examples.

I am lead author of Machine learning for computational fracture and damage mechanics: status and perspectives, published in Engineering Fracture Mechanics with collaborators at EPFL, the University of Florida, and City St George's. The review maps how machine learning is being used across fracture and damage mechanics, while distinguishing credible opportunities from open questions in validation, data, and physical consistency.

Engineering Background

Before doctoral research, I founded Aeroknacks, where I built aerospace structural-analysis and hand-calculation automation tools. That work translated established design references, including E. F. Bruhn, Michael Niu, Boeing Design Manuals, and ESDU data, into practical Excel-VBA and engineering-software workflows. It covered fastener load transfer, lug strength, plastic bending, buckling, composite laminate calculations, and bolted-joint stress fields.

This work shaped the kind of software I enjoy building: technically grounded, transparent, and useful to engineers.

Teaching and Communication

I contribute to engineering teaching at City, including mechanics, materials, manufacturing, wind-turbine structural design, and Siemens NX CAD activities. I also created and maintain open tutorials in PyTorch, neural networks, convolutional neural networks, physics-informed neural networks, and related fundamentals while assisting Dr Sathiskumar Anusuya Ponnusami's teaching at Queen Mary University of London, making the material useful to a wider audience.

My teaching support was recognised with the 2026 School of Science and Technology Dean's Award for Outstanding Teaching Support. I value clear exposition as part of technical work: an implementation or model is more useful when someone else can understand, reproduce, and question it.

Education and Recognition

I hold an MTech in Design and Manufacturing from the National Institute of Technology Silchar, where I graduated with a CGPA of 9.88/10 and received the Best Student Award. I previously completed a B.E. in Mechanical Engineering at GM Institute of Technology, Visvesvaraya Technological University.

My doctoral research was supported by a fully funded City studentship from 2023 to June 2026. I received the 2026 Yeoman and Travelling Scholarship from the Worshipful Company of Tin Plate Workers alias Wire Workers to support conference travel and dissemination. I am also a member of the Royal Aeronautical Society and an Associate Fellow of the Higher Education Academy.

Current Interests

I am particularly interested in roles and collaborations involving differentiable simulation, scientific machine learning, computational mechanics, AI for engineering, and engineering research software. I work primarily with Python, PyTorch, CUDA-capable GPU workflows, finite elements, optimisation, and reproducible research tooling.