Method

DeepONet operator learning

Lu Lu, DeepONet authors · v1.0.0

provenance_verified author_implementation paid_per_execution published

What it does

Learn nonlinear operators between function spaces with DeepONet for parametric PDE surrogates.

When to use it

Learn nonlinear operators between function spaces with DeepONet for parametric PDE surrogates.

Implementation provenance

This MCP is labelled author_implementation. Tools bind to author repository code.

Paper(s)

No papers linked.

Code: https://github.com/lululxvi/deeponet

Licence: See upstream repository

Available MCP tools

`run`

Invoke DeepONet operator learning (catalog entry — wire author MCP or Paper2MCP for full tools)

Provenance: author_provided · deeponet_operator:run

{
  "properties": {
    "problem": {
      "type": "string"
    }
  },
  "type": "object"
}

Example invocation

curl -s -X POST http://127.0.0.1:8765/api/v1/methods/deeponet_operator/execute \
  -H 'Content-Type: application/json' \
  -d '{"tool_name":"run","arguments":{}}'

Validation evidence

provenance_verified: claimed — Upstream repository linked and catalogued. Not execution-verified until MCP tools pass tests.

Price · usage · pay

Current version: deeponet_operator@1.0.0

Price: $0.1200 / execution

Executions: 0 · Creator earnings: $0.00

Citation

Lu Lu, DeepONet authors. DeepONet operator learning (v1.0.0). LemmaMCP method `deeponet_operator`. Implementation: author_implementation. Verification: provenance_verified.