Method

torchdiffeq Neural ODEs

Ricky T. Q. Chen · v1.0.0

provenance_verified author_implementation paid_per_execution published

What it does

Continuous-depth neural ODEs and adjoint sensitivity with torchdiffeq — dynamical systems as networks.

When to use it

Continuous-depth neural ODEs and adjoint sensitivity with torchdiffeq — dynamical systems as networks.

Implementation provenance

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

Paper(s)

No papers linked.

Code: https://github.com/rtqichen/torchdiffeq

Licence: See upstream repository

Available MCP tools

`run`

Invoke torchdiffeq Neural ODEs (catalog entry — wire author MCP or Paper2MCP for full tools)

Provenance: author_provided · torchdiffeq_neural_ode:run

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

Example invocation

curl -s -X POST http://127.0.0.1:8765/api/v1/methods/torchdiffeq_neural_ode/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: torchdiffeq_neural_ode@1.0.0

Price: $0.0800 / execution

Executions: 0 · Creator earnings: $0.00

Citation

Ricky T. Q. Chen. torchdiffeq Neural ODEs (v1.0.0). LemmaMCP method `torchdiffeq_neural_ode`. Implementation: author_implementation. Verification: provenance_verified.