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Python-native multiphysics simulation

Natively coupled, cloud-scale multiphysics fully accessible from your code editor.

AI surrogate training • Automated yield optimization • Agentic engineering

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Quanscient Allsolve

Why Quanscient SDK?

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Massive computational scale

The SDK connects directly to Quanscient's cloud-native solvers, allowing you to run thousands of highly coupled multiphysics simulations concurrently.
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Full programmatic control

Define entire workflows in Python, enabling Git version control, reproducibility, and frictionless collaboration across your R&D department.
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AI-driven optimization

Programmatically extract raw simulation data at scale to build proprietary datasets, train high-fidelity AI surrogate models, that make performance predictions in milliseconds.
Challenges

Limitations with the traditional approach

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The "black box" fragility

Legacy CAE tools were built for desktop UIs, not headless execution. Forcing them into automated loops often results in unresolved crashes, hanging processes, and a lack of machine-readable error codes—breaking autonomous workflows.
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HPC licensing bottlenecks

Traditional tools lock parallel compute behind rigid, core-locked licensing matrices. This makes running the thousands of concurrent simulations required for modern R&D and AI training prohibitively expensive.
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Incompatible architectures for cloud scale

Incumbent APIs rely on outdated bridges—like Java Virtual Machine (JVM) singleton limits or Windows-only COM interfaces—that fundamentally prevent deployment on the cost-effective, massively parallel Linux cloud clusters used in modern tech stacks.
Quanscient Allsolve

Example use cases

Training of AI surrogate models

Orchestrate the generation of tens of thousands of data points to train neural networks, enabling instant performance predictions for complex devices like PMUT arrays or MEMS speakers.

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Automated yield optimization

Script massive Monte Carlo analyses to simulate how manufacturing tolerances impact performance, allowing teams to maximize device yield before physical prototyping begins.

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Agentic engineering & prompt-based workflows

Hook the SDK up to enterprise LLMs to allow autonomous AI agents to write JSON configurations, dispatch simulations, and plot results based on natural language prompts.
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Features

Key features of the Quanscient API

100% GUI parity via Python
Everything you can model in the visual interface, you can command via the SDK. No compromises on geometry, meshing, materials, or physics coupling.
Native JSON & YAML project definitions
Define, track, and modify complete multiphysics setups as structured text files—perfect for LLM integration and strict version control.
Automated parameter sweeps
Define variable ranges natively in your code, and let the SDK autonomously generate and dispatch the entire batch of variations to the cloud.
Programmatic data extraction
Frictionlessly download raw, cloud-solved simulation results directly into Python dataframes (like Pandas) for custom plotting and machine learning ingestion.
Stateless API authentication
Secure, simple setup using modern API keys, making it effortless to integrate simulations into organization-wide CI/CD pipelines.
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Quanscient Allsolve

See how it could work for you

Submit the form to talk with our experts—we'll respond within 1 business day. You'll learn:

  • How Allsolve could fit your use case
  • What results to expect (accuracy, runtime, design exploration capabilities and rough cost range)
  • How it could plug into your workflow today

Interested in just seeing an on-demand demo? Watch the 3-minute demo here