Manish Agrawal

Mechanical department, IIT, Ropar, manish.agrawal@iitrpr.ac.in

Research Interests

Finite element analysis, Continuum mechanics, Deep learning applied to mechanical systems, Multi-Physics simulations

Teaching Interests

Finite element analysis, Deep learning for physical systems, Vibration and control, Continuum Mechanics, Chaos for dynamic systems, MultiBody Dynamics.

Ongoing Projects:

  1. Development of deep learning tool for the prediction of composite material properties( In collaboration with Dr. Prabhat): The pre print of the work can be found here.

  2. Development of surgical tool based simulation platform based on ML/AI

  3. Developing finite element strategy for contact mechanics problems

  4. AI-based tool for detection of mine using GPR scan data.(In collaboration with Dr. Srikant)

  5. Development of finite element strategy for droplet impact on soft substrate. (In collaboration with Dr. Chander )

  6. Development of AI strategy for hydrogen embrittlement problem. (In collaboration with Dr. Dhiraj)

  7. Development of the mixed finite element strategy for functionally graded materials. (In collaboration with Dr. Srikant)

  8. Development of the AI strategy for bio mechanics applications (In collaboration with Dr. Navin)

  9. Development of the finite element strategies for the fatigue applications (In collaboration with Dr. Sachin)

  10. Development of the active learning strategy for deep learning application(In collaboration with Dr. Shweta Jain, Computer science department)

Ongoing Collaboration (Outside IIT, Ropar):

  1. Dr. Anirban (Mechanical, IIT Bhabaneswar): Development of deep learning framework for optimizing the two phase storage system.

  2. Dr. Arup (Mechanical, IIT Guwahati): Development of hybrid elements for the isogeometric analysis.

  3. Suman Dutta (Mechanical, IISc Bangalore): Monolithic fluid structure interaction strategy for the floating bodies .

  4. Dr. Adway Mitra (Computer science, IIT Kharagpur): Solving partial differential equations using deep learning.

  5. Dr. Arup (Mechanical, IIT Guwahati): Optimization of electro-forming operations using the deep learning techniques.

Publications:

  • D. S. Bombardea, M. Agrawal, S. S. Gautama, A. Nandy, Hellinger–Reissner principle based stress–displacement formulation for three-dimensional isogeometric analysis in linear elasticity, Computer Methods in Applied Mechanics and Engineering, [Accepted, under publication], 2022.

  • M. Agrawal, A. Nandy, and C.S. Jog. A hybrid finite element formulation for large-deformation contact mechanics. Computer Methods in Applied Mechanics and Engineering, 356:407 – 434, 2019.

  • M. Agrawal and C. S. Jog. Monolithic formulation of electromechanical systems within the contextof hybrid finite elements. Computational Mechanics, 59(3):443–457, 2017.

  • M. Agrawal and C. S. Jog. A quadratic time finite element method for nonlinear elastodynamicswithin the context of hybrid finite elements.Applied Mathematics and Computation, 305:203–220, 2017

  • C. S. Jog, M. Agrawal, and A. Nandy. The time finite element as a robust general scheme for solving nonlinear dynamic equations including chaotic systems.Applied Mathematics and Computation,279:43–61, 2016.

  • M. Agrawal and G.K. Ananthasuresh. “On Including Manufacturing Constraintsin the Topology Optimization of Surface-Micromachined Structures”.7th WorldCongress on Structural and Multidisciplinary Optimization, Seoul, 2007