Deepak Kumar Pokkalla

I'm a senior research scientist at Dassault Systèmes in Boston, where I work on scientific machine learning (SciML). Previously, I was a research associate at ORNL and was a recipient of 2023 R&D100 Technology Award (the "Nobel Prize of Engineering").

I did my PhD in computational mechanics at the National University of Singapore, where I was advised by Prof. Poh Leong Hien and Prof. Quek Ser Tong. I obtained my bachelors degree from Indian Institute of Technology (IIT) Varanasi and was honored with IIT (BHU) Varanasi Gold medal.

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I'm interested in applying machine learning techniques (transformers, graph networks, diffusion models) for real world problems involving computational geometry and scientific/engineering simulations. My current focus is on physics-based machine learning and geometric deep learning.

I've expertise in computer-aided design/engineering (CAD/CAE) and have hands-on experience in developing and applying nonlinear simulation and optimization techniques across multiscale and multiphysics domains (structural, thermal, CFD). I also posses strong knowledge of computational solid mechanics, finite element analysis (FEA), high performance computing (HPC), mechanics of materials, and additive manufacturing/3D printing of composites and metals.

Developed a novel isogeometric shape optimization framework for the design of auxetic materials with prescribed nonlinear mechanical responses. It involved developing code for nonlinear isogeometic analysis and gradient-based/gradient-free optimization frameworks. The capability of these frameworks is illustrated through additive manufacturing of optimized materials/structures and experimental validations.

We introduce a deep residual networks-based design framework for rapid inverse design of mechanical metamaterials with desired nonlinear mechanical responses. Our framework rapidly explores the topological design space through efficient numerical analysis and leverages the learned structure-property relationships. We illustrate the applicability of our framework by identifying previously unknown designs with enhanced mechanical performance.

We introduce an efficient deep neural networks based design framework for functionally graded lattices with enhanced mechanical responses. We demonstrate the capability and versatility of our framework by designing multiple lattice structures and experiments on additively manufactured optimized lattices.

We introduce symmetry breaking in bistable kirigami to access geometric frustration and anisotropic morphing, enabling arbitrarily scaled deployment in planar and spatial bistable domains. Our approach involves nonlinear numerical simulations (finite element analysis), design optimization, and experiments on elastic kirigami sheets to unlock anisotropic bistable deployment in planer and flat-to-3D configurations for deployable space structures, wearable technologies, and soft machines.

We employ a gradient-free genetic algorithm based isogeometric optimization framework to design soft network materials with desired nonlinear mechanical responses and fabricate them using a liquid additive manufacturing for biomedical applications.

We present an isogeometric shape optimization framework using genetic algorithm to design metamaterials with prescribed nonlinear mechanical responses. Through a combination of nonlinear numerical simulations and experiments, we demonstrate the design and manufacturability of soft flexible materials under a range of loading conditions.

We present a novel gradient-based isogeometric shape optimization framework for designing metamaterials with prescribed deformation over large strains, considering evolving geometric shapes and manufacturability constraints. We illustrate the capability of our framework by designing auxetic materials under different loading conditions and by reproducing the deformation behaviour of cat's skin.

We present the first numerical analysis upon the effect of heterogeneity on the stresses in fretting fatigue problems. We analyze the stress distribution in heterogeneous material under fretting fatigue loading condition based on Direct Numerical Simulations (DNS) in solid mechanics.

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