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🥭 MaNGO: Adaptable Graph Network Simulators via Meta-Learning

This repository will host the official code release for our NeurIPS 2025 paper:

MaNGO: Adaptable Graph Network Simulators via Meta-Learning
Philipp Dahlinger, Tai Hoang, Denis Blessing, Niklas Freymuth, Gerhard Neumann Karlsruhe Institute of Technology (KIT)

Check out our project page for an overview and visualizations of all tasks!

Overview

MaNGO introduces a Meta Neural Graph Operator that enables Graph Network Simulators to adapt across different physical systems.
The method combines meta-learning with a neural operator based architecture, allowing the simulator to generalize to unseen material properties and predict full trajectories efficiently and stably.

Installation

We use uv as our environment manager. To set up the environment, run:

# Create a virtual environment (recommended)
uv venv
uv sync

For developing access, run:

uv pip install -e .

Usage

If you just want to have a look at the decoder code as a baseline for your own experiments, check it out in src/mango/simulator/ml_decoder/mango_decoder.py.

For training the full MaNGO model on the datasets, you can use the training script train.py. You need to provide a hydra config file. As an example, you can run

uv python train.py +experiment/final_exp/cnn_deepset_mango=dp_easy_v5 +platform=local_multirun

For that to work you need to download the dataset here: Dataset Download Link. Put the hdf5 files into a folder ../datasets/mango/ relative to the root of this repository (or update the path in the dataset configs in configs/dataset/)

Contact

For questions, please contact:
Philipp Dahlinger – philipp.dahlinger@kit.edu

About

MaNGO - Adaptable Graph Network Simulators via Meta-Learning

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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🥭 MaNGO: Adaptable Graph Network Simulators via Meta-Learning
This repository will host the official code release for our NeurIPS 2025 paper:
MaNGO: Adaptable Graph Network Simulators via Meta-Learning

Philipp Dahlinger, Tai Hoang, Denis Blessing, Niklas Freymuth, Gerhard Neumann
Karlsruhe Institute of Technology (KIT)
Check out our project page for an overview and visualizations of all tasks!
Overview
MaNGO introduces a Meta Neural Graph Operator that enables Graph Network Simulators to adapt across different physical systems.

The method combines meta-learning with a neural operator based architecture, allowing the simulator to generalize to unseen material properties and predict full trajectories efficiently and stably.
Installation
We use uv as our environment manager. To set up the environment, run:
# Create a virtual environment (recommended)
uv venv
uv sync
For developing access, run:
uv pip install -e .
Usage
If you just want to have a look at the decoder code as a baseline for your own experiments, check it out in src/mango/simulator/ml_decoder/mango_decoder.py.
For training the full MaNGO model on the datasets, you can use the training script train.py. You need to provide a hydra config file. As an example, you can run
uv python train.py +experiment/final_exp/cnn_deepset_mango=dp_easy_v5 +platform=local_multirun
For that to work you need to download the dataset here: Dataset Download Link.
Put the hdf5 files into a folder ../datasets/mango/ relative to the root of this repository (or update the path in the dataset configs in configs/dataset/)
Contact
For questions, please contact:
Philipp Dahlinger – philipp.dahlinger@kit.edu
About
MaNGO - Adaptable Graph Network Simulators via Meta-Learning
Resources
Readme
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1 watching
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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🥭 MaNGO: Adaptable Graph Network Simulators via Meta-Learning
This repository will host the official code release for our NeurIPS 2025 paper:
MaNGO: Adaptable Graph Network Simulators via Meta-Learning

Philipp Dahlinger, Tai Hoang, Denis Blessing, Niklas Freymuth, Gerhard Neumann
Karlsruhe Institute of Technology (KIT)
Check out our project page for an overview and visualizations of all tasks!
Overview
MaNGO introduces a Meta Neural Graph Operator that enables Graph Network Simulators to adapt across different physical systems.

The method combines meta-learning with a neural operator based architecture, allowing the simulator to generalize to unseen material properties and predict full trajectories efficiently and stably.
Installation
We use uv as our environment manager. To set up the environment, run:
# Create a virtual environment (recommended)
uv venv
uv sync
For developing access, run:
uv pip install -e .
Usage
If you just want to have a look at the decoder code as a baseline for your own experiments, check it out in src/mango/simulator/ml_decoder/mango_decoder.py.
For training the full MaNGO model on the datasets, you can use the training script train.py. You need to provide a hydra config file. As an example, you can run
uv python train.py +experiment/final_exp/cnn_deepset_mango=dp_easy_v5 +platform=local_multirun
For that to work you need to download the dataset here: Dataset Download Link.
Put the hdf5 files into a folder ../datasets/mango/ relative to the root of this repository (or update the path in the dataset configs in configs/dataset/)
Contact
For questions, please contact:
Philipp Dahlinger – philipp.dahlinger@kit.edu
About
MaNGO - Adaptable Graph Network Simulators via Meta-Learning
Resources
Readme
Activity
Custom properties
Stars
11 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
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Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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🥭 MaNGO: Adaptable Graph Network Simulators via Meta-Learning
This repository will host the official code release for our NeurIPS 2025 paper:
MaNGO: Adaptable Graph Network Simulators via Meta-Learning

Philipp Dahlinger, Tai Hoang, Denis Blessing, Niklas Freymuth, Gerhard Neumann
Karlsruhe Institute of Technology (KIT)
Check out our project page for an overview and visualizations of all tasks!
Overview
MaNGO introduces a Meta Neural Graph Operator that enables Graph Network Simulators to adapt across different physical systems.

The method combines meta-learning with a neural operator based architecture, allowing the simulator to generalize to unseen material properties and predict full trajectories efficiently and stably.
Installation
We use uv as our environment manager. To set up the environment, run:
# Create a virtual environment (recommended)
uv venv
uv sync
For developing access, run:
uv pip install -e .
Usage
If you just want to have a look at the decoder code as a baseline for your own experiments, check it out in src/mango/simulator/ml_decoder/mango_decoder.py.
For training the full MaNGO model on the datasets, you can use the training script train.py. You need to provide a hydra config file. As an example, you can run
uv python train.py +experiment/final_exp/cnn_deepset_mango=dp_easy_v5 +platform=local_multirun
For that to work you need to download the dataset here: Dataset Download Link.
Put the hdf5 files into a folder ../datasets/mango/ relative to the root of this repository (or update the path in the dataset configs in configs/dataset/)
Contact
For questions, please contact:
Philipp Dahlinger – philipp.dahlinger@kit.edu
About
MaNGO - Adaptable Graph Network Simulators via Meta-Learning
Resources
Readme
Activity
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Stars
11 stars
Watchers
1 watching
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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🥭 MaNGO: Adaptable Graph Network Simulators via Meta-Learning
This repository will host the official code release for our NeurIPS 2025 paper:
MaNGO: Adaptable Graph Network Simulators via Meta-Learning

Philipp Dahlinger, Tai Hoang, Denis Blessing, Niklas Freymuth, Gerhard Neumann
Karlsruhe Institute of Technology (KIT)
Check out our project page for an overview and visualizations of all tasks!
Overview
MaNGO introduces a Meta Neural Graph Operator that enables Graph Network Simulators to adapt across different physical systems.

The method combines meta-learning with a neural operator based architecture, allowing the simulator to generalize to unseen material properties and predict full trajectories efficiently and stably.
Installation
We use uv as our environment manager. To set up the environment, run:
# Create a virtual environment (recommended)
uv venv
uv sync
For developing access, run:
uv pip install -e .
Usage
If you just want to have a look at the decoder code as a baseline for your own experiments, check it out in src/mango/simulator/ml_decoder/mango_decoder.py.
For training the full MaNGO model on the datasets, you can use the training script train.py. You need to provide a hydra config file. As an example, you can run
uv python train.py +experiment/final_exp/cnn_deepset_mango=dp_easy_v5 +platform=local_multirun
For that to work you need to download the dataset here: Dataset Download Link.
Put the hdf5 files into a folder ../datasets/mango/ relative to the root of this repository (or update the path in the dataset configs in configs/dataset/)
Contact
For questions, please contact:
Philipp Dahlinger – philipp.dahlinger@kit.edu
About
MaNGO - Adaptable Graph Network Simulators via Meta-Learning
Resources
Readme
Activity
Custom properties
Stars
11 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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🥭 MaNGO: Adaptable Graph Network Simulators via Meta-Learning
This repository will host the official code release for our NeurIPS 2025 paper:
MaNGO: Adaptable Graph Network Simulators via Meta-Learning

Philipp Dahlinger, Tai Hoang, Denis Blessing, Niklas Freymuth, Gerhard Neumann
Karlsruhe Institute of Technology (KIT)
Check out our project page for an overview and visualizations of all tasks!
Overview
MaNGO introduces a Meta Neural Graph Operator that enables Graph Network Simulators to adapt across different physical systems.

The method combines meta-learning with a neural operator based architecture, allowing the simulator to generalize to unseen material properties and predict full trajectories efficiently and stably.
Installation
We use uv as our environment manager. To set up the environment, run:
# Create a virtual environment (recommended)
uv venv
uv sync
For developing access, run:
uv pip install -e .
Usage
If you just want to have a look at the decoder code as a baseline for your own experiments, check it out in src/mango/simulator/ml_decoder/mango_decoder.py.
For training the full MaNGO model on the datasets, you can use the training script train.py. You need to provide a hydra config file. As an example, you can run
uv python train.py +experiment/final_exp/cnn_deepset_mango=dp_easy_v5 +platform=local_multirun
For that to work you need to download the dataset here: Dataset Download Link.
Put the hdf5 files into a folder ../datasets/mango/ relative to the root of this repository (or update the path in the dataset configs in configs/dataset/)
Contact
For questions, please contact:
Philipp Dahlinger – philipp.dahlinger@kit.edu
About
MaNGO - Adaptable Graph Network Simulators via Meta-Learning
Resources
Readme
Activity
Custom properties
Stars
11 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
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Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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🥭 MaNGO: Adaptable Graph Network Simulators via Meta-Learning
This repository will host the official code release for our NeurIPS 2025 paper:
MaNGO: Adaptable Graph Network Simulators via Meta-Learning

Philipp Dahlinger, Tai Hoang, Denis Blessing, Niklas Freymuth, Gerhard Neumann
Karlsruhe Institute of Technology (KIT)
Check out our project page for an overview and visualizations of all tasks!
Overview
MaNGO introduces a Meta Neural Graph Operator that enables Graph Network Simulators to adapt across different physical systems.

The method combines meta-learning with a neural operator based architecture, allowing the simulator to generalize to unseen material properties and predict full trajectories efficiently and stably.
Installation
We use uv as our environment manager. To set up the environment, run:
# Create a virtual environment (recommended)
uv venv
uv sync
For developing access, run:
uv pip install -e .
Usage
If you just want to have a look at the decoder code as a baseline for your own experiments, check it out in src/mango/simulator/ml_decoder/mango_decoder.py.
For training the full MaNGO model on the datasets, you can use the training script train.py. You need to provide a hydra config file. As an example, you can run
uv python train.py +experiment/final_exp/cnn_deepset_mango=dp_easy_v5 +platform=local_multirun
For that to work you need to download the dataset here: Dataset Download Link.
Put the hdf5 files into a folder ../datasets/mango/ relative to the root of this repository (or update the path in the dataset configs in configs/dataset/)
Contact
For questions, please contact:
Philipp Dahlinger – philipp.dahlinger@kit.edu
About
MaNGO - Adaptable Graph Network Simulators via Meta-Learning
Resources
Readme
Activity
Custom properties
Stars
11 stars
Watchers
1 watching
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0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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🥭 MaNGO: Adaptable Graph Network Simulators via Meta-Learning
This repository will host the official code release for our NeurIPS 2025 paper:
MaNGO: Adaptable Graph Network Simulators via Meta-Learning

Philipp Dahlinger, Tai Hoang, Denis Blessing, Niklas Freymuth, Gerhard Neumann
Karlsruhe Institute of Technology (KIT)
Check out our project page for an overview and visualizations of all tasks!
Overview
MaNGO introduces a Meta Neural Graph Operator that enables Graph Network Simulators to adapt across different physical systems.

The method combines meta-learning with a neural operator based architecture, allowing the simulator to generalize to unseen material properties and predict full trajectories efficiently and stably.
Installation
We use uv as our environment manager. To set up the environment, run:
# Create a virtual environment (recommended)
uv venv
uv sync
For developing access, run:
uv pip install -e .
Usage
If you just want to have a look at the decoder code as a baseline for your own experiments, check it out in src/mango/simulator/ml_decoder/mango_decoder.py.
For training the full MaNGO model on the datasets, you can use the training script train.py. You need to provide a hydra config file. As an example, you can run
uv python train.py +experiment/final_exp/cnn_deepset_mango=dp_easy_v5 +platform=local_multirun
For that to work you need to download the dataset here: Dataset Download Link.
Put the hdf5 files into a folder ../datasets/mango/ relative to the root of this repository (or update the path in the dataset configs in configs/dataset/)
Contact
For questions, please contact:
Philipp Dahlinger – philipp.dahlinger@kit.edu
About
MaNGO - Adaptable Graph Network Simulators via Meta-Learning
Resources
Readme
Activity
Custom properties
Stars
11 stars
Watchers
1 watching
Forks
0 forks
Report repository
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Contributors
Languages