I recently visited two customers (bioprocess) consecutively for AI/ML-related demos, and interestingly, both of them raised the same question. Both customers are currently exploring digital twin approaches — specifically, generating simulation data to supplement limited experimental datasets.
1. Have you seen any cases — or do you have experience — generating simulation data generated from a mechanistic model ?
For some context, a mechanistic model is a mathematical representation of a process based on fundamental physical, chemical, and biological principles — rather than empirical data. It allows us to generate simulated process data purely from first principles, without running actual experiments.
In bioprocess purification, typical examples include:
- Chromatography → van Deemter equation, mass transfer equations
- Ultrafiltration/Diafiltration (UF/DF) → Darcy's law, concentration polarization models
Any insight would be helpful for me.
On my end, when customers asked about building mechanistic models, my initial suggestion was to implement them in Python and integrate Python with JMP. but i am wondering if there are more deeper ways to help them.
2.
Since real experimental data is limited, the plan is to generate a large dataset using a mechanistic model. However, this simulation data will inevitably carry some error compared to real experiments.
What are the recommended approaches to correct or compensate for this error — so that the AI model trained on this data is still reliable?
Any known methods or case studies in JMP would be appreciated!
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