LLM-Based Structural Optimization Coding
Budget: $250 – $750 USD
I have an ongoing structural-optimization study that will be powered by a large language model. Your role is to help me develop and train the machine-learning model, integrate it with my structural analysis routines, and write clean, reproducible code in both Python and MATLAB.
The core of the job is model training: we will fine-tune or custom-train an LLM so it can suggest improved structural layouts, then feed those suggestions into an optimization loop that runs finite-element calculations already available on my side. I will supply the data sets, initial scripts, and target performance metrics. You will extend the codebase, implement the training pipeline, and link everything to the optimization module.
Deliverables
• Python scripts and MATLAB functions that load data, train the LLM, and call the structural optimization routine.
• Clear documentation (inline comments and a short README) so I can rerun the experiments on my workstation.
• A brief report summarizing training results and the improvement achieved in the optimized designs.
All work must execute on standard Python 3.x with PyTorch (or TensorFlow, if preferred) and MATLAB R2021a+. Provide any additional open-source libraries in a requirements list.
Once the code reproduces the baseline accuracy and demonstrates a measurable structural-performance gain, the project is considered complete and ready for the next research stage.
The core of the job is model training: we will fine-tune or custom-train an LLM so it can suggest improved structural layouts, then feed those suggestions into an optimization loop that runs finite-element calculations already available on my side. I will supply the data sets, initial scripts, and target performance metrics. You will extend the codebase, implement the training pipeline, and link everything to the optimization module.
Deliverables
• Python scripts and MATLAB functions that load data, train the LLM, and call the structural optimization routine.
• Clear documentation (inline comments and a short README) so I can rerun the experiments on my workstation.
• A brief report summarizing training results and the improvement achieved in the optimized designs.
All work must execute on standard Python 3.x with PyTorch (or TensorFlow, if preferred) and MATLAB R2021a+. Provide any additional open-source libraries in a requirements list.
Once the code reproduces the baseline accuracy and demonstrates a measurable structural-performance gain, the project is considered complete and ready for the next research stage.
Related categories:
Python
Matlab and Mathematica
Structural Engineering
Machine Learning (ML)
MATLAB
Large Language Model