Lakshmi Sharma

Applied Artificial Intelligence for vehicle aerodynamics. This project saved ≈50% development process time of Computational Fluid Dynamics (CFD) Engineers and reduced the cost of the aerodynamics process. - Implemented AI algorithms over 3D geometries of pattern-recognized data with the goal of predicting flow fields using the TensorFlow framework, which eliminates ≈80% of manual work.

- Computational Fluid Dynamics simulations were tried to solve using Machine learning rather than applying traditional numerical methods (Finite Elements and so on). - Designed, implemented, optimized, tested and analysis of machine learning models. - Most of the work performed focused on state-of-the-art Physics -informed- Neural Networks (PINN) with the aim of solving partial differential equations. - Analyze and validate concepts based on simulations and real data.

- Extracted, visualized and optimized organoids data (Images) for Machine Learning (ML/AI). - Experience in building, integrating, and maintaining data pipelines and machine learning algorithms. - Implemented supervised multi class classification (SVM, KNN, Convolutional Neural Networks and other algorithms) and Generative Adversarial Networks using Python.

- Automated the functionality to download the files using python from PTC server to the git repository. - Automated the Statistics results for Tracking and cost optimization of licenses through extracting data from log files - Automatic Email generation and user notification about missing equipment.