About me
Hi! I’m Ravinder Bhattoo, assistant professor in the Department of Civil Engineering at the Indian Institute of Technology Indore. My research focus areas are machine learning-aided structural design, multiscale material modeling, physics-informed machine learning, graph neural networks, dynamic fracture and crack propagation on ballistic impact, molecular dynamics, and peridynamics. Previously, I have worked as a postdoctoral scholar in the Department of Civil and Environmental Engineering at the University of Wisconsin-Madison and as an early doc scholar in the Department of Civil Engineering at the Indian Institute of Technology Delhi. I earned my Ph.D. in Civil Engineering from the Indian Institute of Technology Delhi in January 2023, and my B. Tech. in Civil Engineering from the Indian Institute of Technology Roorkee in June 2015. My Ph.D. research focused on data-driven modeling and physics-informed machine learning for glass discovery.
Some recent work
Post: PeriDyn is a numerical simulation software designed to solve peridynamics problems. It is written in the Julia programming language, and offers a high level of flexibility and speed. PDBenchmark is built on top of the PeriDyn package, which provides a number of predefined material models and benchmark problems. This allows users to quickly set up and run simulations, and compare their results to established benchmarks. Read more
Publication: Physical systems are commonly represented as a combination of particles, the individual dynamics of which govern the system dynamics. However, traditional approaches require the knowledge of several abstract quantities such as the energy or force to infer the dynamics of these particles. Here, we present a framework, namely, Lagrangian graph neural network (LGnn), that provides a strong inductive bias to learn the Lagrangian of a particle-based system directly from the trajectory. We test our... Bhattoo, Ravinder; Ranu, Sayan; Krishnan, N. M. Anoop Machine Learning: Science and Technology Read more
Publication: Despite the use of inorganic glasses for more than 4500 years, the composition–property relationships in these materials remain poorly understood. Here, exploiting largescale experimental data and machine learning, we develop composition–property models for twenty five properties, which are interpreted using game-theoretic concepts. Specifically, we use a dataset consisting of ∼275,000 glass compositions comprising of 221 different components and 25 properties. This is by far the largest mode... Bhattoo, Ravinder; Bishnoi, Suresh; Zaki, Mohd; Krishnan, N. M. Anoop Acta Materialia Read more