Scientific ML · Biotech & Pharma
Physics-Informed Neural Networks (PINNs) for Biotech & Pharma
Apply the physics-informed neural networks (pinns) to real biotech & pharma problems — in the browser, with AI assistance.
PINNs embed PDE residuals in the loss so the network learns solutions consistent with physics from sparse data.
In biotech & pharma, teams face challenges like reaction kinetics, mixing and transport, regulatory validation. The physics-informed neural networks (pinns) directly supports use cases such as enzyme kinetics, bioreactor mixing cfd, drug-diffusion modeling, and PolySim's AI Copilot can recommend settings and catch common setup errors before you run.
Typical Biotech & Pharma use cases
- Enzyme kinetics
- Bioreactor mixing CFD
- Drug-diffusion modeling
Upgrade your workspace
Educator Classroom Kit — $45
Set up a class of 30 students for one term with shared projects.
Recommended products
Physics Starter Kit
$19Everything you need to start simulating classical & modern physics.
Solver Validation & Benchmarking
$149Validate your setup against benchmark cases and report accuracy.
Outcome / Accuracy Assurance Review
$15A specialist sanity-checks a result before you rely on it.
FEA Masterclass
$25An in-depth masterclass on finite-element analysis.
API / Developer Plan
$99/moHigh API limits and webhooks for building on PolySim.
Quick-Sim Credit ×5
$4Five cloud simulation runs for quick iteration on the go.