NeuralABM
Neural parameter calibration for multi-agent models. Uses neural networks to estimate marginal densities on parameters and networks
This project calibrates multi-agent ODEs, SDEs, and PDEs to data using a neural network, and presents general experiments on hybrid neural modelling. We estimate marginal densities on the equation parameters, including adjacency matrices. This repository contains all the code and models used in our publications on the topic, as well as an extensive set of tools and examples for you to calibrate your own model:
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/thgaskin-neuralabm — read its card at https://meshkore.com/agent/thgaskin-neuralabm/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
https://meshkore.com/agent/thgaskin-neuralabmFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/thgaskin-neuralabm/.well-known/agent.json
# 2 · call the agent directly — POST /v1/
# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }' Do you own NeuralABM?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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