Über Shakeel Mohammed
Simulation Engineer and ML Developer : FEM · CFD · AI Surrogates
I help engineers get faster answers from their simulations by combining FEM, CFD, and machine learning in one workflow.
What I do:
- FEM & Structural Simulation: Structural, thermal, and vibration analysis using Ansys, FEniCSx, and CalculiX. Including experimental validation and digital twin integration.
- CFD & Flow Analysis: Flow simulations with OpenFOAM — from meshing to post-processing.
- Machine Learning
- Surrogate models that replace slow FEM/CFD runs
- PINNs — physics-based neural networks that work with little or no data
- Neural operators (FNO, DeepONet) for fast multi-case predictions
- Inverse problems to find material properties from measurements
- Image analysis : microstructure segmentation and defect detection
- Time-series prediction : vibration and sensor data with LSTM
- Deployment: PyTorch → ONNX → C++. HPC-ready
Typical projects:
- Too many simulation runs? → I build a surrogate that replaces them
- Unknown material parameters? → I set up an inverse PINN to identify them
- Microstructure images to analyze? → I train a segmentation model
- Simulation data sitting unused? → I turn it into a prediction tool
Stack: Python · PyTorch · Julia · Ansys · FEniCSx · OpenFOAM · ONNX · C++ · MPI · OpenCV · Fortran
Strong in the overlap between physics and AI: a combination most engineers and most ML developers don't cover alone.
Englisch
Muttersprachlich oder zweisprachig
Deutsch
Konversationssicher
Nur remote
Führt Projekte hauptsächlich remote aus
Projekt- und Berufserfahrung
- Spektra gmbh dresdenResearch AssociateFORSCHUNGNovember 2024 - Mai 2025 (6 Monate)Dresden, Deutschland- Developed a CAD-to-FEM simulation workflow to analyze unintended transverse vibrations in the SPEKTRA SE-21 electrodynamic shaker using PrePoMax (pre/post) and CalculiX (solver), including modal and harmonic response analyses (u₁/u₂, u₃/u₂).- Performed systematic tolerance and imperfection studies (local stiffness changes, assembly deviations, magnetic-force imbalance, gravity/orientation effects) and automated evaluation/sensitivity ranking using Python.- Validated simulation findings against experimental/geometry inputs by comparing with laser vibration measurement data and 3D-scan–based deviation data with Opencv and plotly, translating results into design/manufacturing insights.
- Fraunhofer IWUResearch assistantFORSCHUNGApril 2024 - September 2024 (5 Monate)Dresden, Deutschland- Automated FEM simulation studies and result extraction using Ansys Workbench + Python (PyAnsys) to generate consistent datasets and speed up parametric investigations.- Designed and trained Deep Learning models in PyTorch for prediction on simulation/measurement datasets, including preprocessing, training, and evaluation and convert it onnx format for c++ integration.- Built sdk of digital twin by integrating onnx model with C++ components.
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Zertifizierungen
- PyTorch for Deep LearningDeeplearning.ai2026