Vaishali Ravishankar

Product Engineer

Cologne, Germany2 yrs 5 mos experience

Most Likely To Switch

Key Highlights

Expert in advanced computational techniques for engineering problems.
Proficient in deep learning models for physics applications.
Strong background in CFD and optimization methodologies.

Stackforce AI infers this person is a Computational Engineer specializing in CFD and AI-driven optimization.

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Skills

Core Skills

Bayesian OptimizationCfdGraph Neural NetworksMathematical Modeling

Other Skills

Data AnalysisPythonParaViewLarge Language ModelsPyTorchOpenFOAMScikit-LearnFreeCADShell ScriptingResearch SkillsGitLinuxStar-CCM+JavaC++

About

As a Mechanical Engineer with a Master's in Computational Science, I specialize in applying advanced computational techniques to solve complex engineering problems. My areas of expertise include Optimization, Numerical Methods, Scientific Machine Learning, Data Analysis, CFD, and HPC. My academic journey was driven by a passion for computational modeling using numerical solvers as well as developing deep learning models for physics (PINNs). My Master’s thesis focused on the exploratory analysis of turbulent flow data using a GNN-based surrogate model. My technical skills include Python (PyTorch, Tensorflow, Scikit-learn, Pandas) , C++, CUDA, Java, and SQL, along with proficiency in simulation tools such as OpenFOAM, ANSYS Fluent and Star CCM+. Passionate about integrating research with practical applications, I am eager to connect with researchers, professionals, and enthusiasts, actively exploring advanced computational technologies at the intersection of CFD, optimization, and deep learning.

Experience

2 yrs 5 mos

Total Experience

1 yr 2 mos

Average Tenure

1 yr 6 mos

Current Experience

Fraunhofer scai

2 roles

Scientific Researcher

Dec 2024 – Present · 1 yr 6 mos · Sankt Augustin, North Rhine-Westphalia, Germany · On-site

Research group: Numerical Data-Driven Prediction
Implemented constrained Bayesian Optimization for single and multi objective black box design problems encountered in aerospace applications.
Conducted data analysis using PyVista and in-house dimensionality reduction and visualization tools, to extract patterns and insights from complex engineering datasets.
Streamlined CFD workflows with ParaView, Python, and batch processing, enhancing efficiency and resource management in large-scale simulations.
Implemented Convolutional and Graph Neural Networks for fast predictions of CFD simulations.
Fine-tuned large language and vision-language models (LLMs/VLMs) on automotive datasets to enhance domain-specific AI capabilities.

Bayesian OptimizationData AnalysisCFDPythonParaViewGraph Neural Networks+1