DIVAKAR RAJU P V


DIVAKAR RAJU P V

Divakar Raju P V is an accomplished Mechanical Engineering Ph.D. graduate from IIT Tirupati, specializing in composite materials. He has pioneered in the microscale characterization of flax fiber and developed advanced numerical and machine learning models for composite materials’ analysis. With extensive research, teaching, and supervisory experience, he possesses skills in composite manufacturing, testing, finite element analysis, machine learning and technical writing. He published significant research papers in reputed peer reviewed journals and presented at prestigious international conferences.

Top Skills

  • Abaqus
  • Ansys Products
  • ASTM standards
  • Python (Programming Language)
  • Composite Structures

LinkedIn Profile

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Indian Institute of Technology, Tirupati logo
  • Indian Institute of Technology, Tirupati · Full-time
    Jul 2019 – Present · 5 yrs 2 mos
    India

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Indian Institute of Technology, Tirupati logo
  • Doctor of Philosophy, Mechanical Engineering
    2019 – 2024
Jawaharlal Nehru Technological University, Anantapur logo
  • Master of Technology
    2014 – 2016
Rajiv Gandhi University of Knowledge Technologies, RKValley (RAC) logo
  • Bachelor of Technology, Mechanical Engineering
    2010 – 2014

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Machine Learning for Composite Materials with Abaqus

Original price was: € 380.0.Current price is: € 342.0.

This tutorial package delves into an advanced inverse modeling approach for predicting carbon fiber properties in composite materials using a machine learning (ML) technique. Specifically, it covers the use of Gaussian Process Regression (GPR) to build a surrogate model for accurate predictions of fiber properties based on data from unidirectional (UD) lamina. By leveraging Finite Element (FE) homogenization, synthetic data is generated for training the GPR model, accounting for variations in fiber, matrix properties, and volume fractions. This framework’s efficiency and accuracy are validated using real-world data, highlighting its potential as a computational alternative to traditional experimental methods. The package includes detailed explanations, case studies, and practical exercises, equipping users with hands-on experience in applying this ML-based approach to composite material analysis.

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