0_Basic_Competition_Information.ipynb

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    "# Competition overview\n",
    "\n",
    "<b> Context: </b> To tackle climate change, renewables energies such as solar and wind need to be massively integrated  in the years to come into the power grid to achieve decarbonation. This brings complex new operational challenges.\n",
    "\n",
    "<b> Industrial Challenge: </b> Due to their intermittency, many more scenarios need to be studied to mitigate risks near real-time to keep the grid safe without cascading failures. In particular, the operators need to anticipate what could happen in terms of congestions on the grid in case some unexpected outage occurs on some power lines under different wind and solar conditions. Hence the need for powerflow simulation is exploding at least 1000-fold in control rooms.\n",
    "\n",
    "<b> Objective: </b> This competition aims at unlocking new methods for speeding-up these simulations by several order of magnitude, while ensuring some realistic criteria, in order to run this more complex risk assessment tomorrow. You will apply this on a grid of similar size of one controlled by a human operator with an energy mix that include 30% wind and solar as expected on the French grid in the near future.\n",
    "\n",
    "<p style=\"text-align: center;\">\n",
    "  <img src=\"https://i.ibb.co/5njDgfc/illustration.gif\" height=\"400\" style=\"margin-right: 10px\" />\n",
    "</p>\n",
    "\n",
    "[//]: #  (The overall aim in this challenge is to build innovative ML surrogate models to compute the grid power flows, while finding the best trade-off between the inference precision and computation costs.)\n",
    "\n",
    "\n",
    "<b> Scope: </b> The scope of the competition is to:\n",
    "\n",
    "* Promote the use of ML surrogate models for power grid systems;\n",
    "* Promote reproducibility, transparency, and industrial relevance by incorporating a dedicated challenge\n",
    "phase focused on end-to-end reproducibility of machine learning for physics. Participants will therefore be required to submit untrained models, which will then be fully retrained and\n",
    "evaluated on our servers. This will ensure that the models are reproducible and can be tested\n",
    "independently, thereby enhancing the transparency of the entire process;\n",
    "* Encourage finding synergies in terms of solutions for different physical problems, in particular in relationship with the previous edition of the ML4PhySim challenge about the AirFrans use case\n",
    "* Encourage collaboration between AI and physical sciences by designing new ML approaches\n",
    "(algorithms, architectures) targeting physical problems;\n",
    "* Ensure a meaningful evaluation of the submitted ML surrogate models thanks to a homogeneous\n",
    "comparison of different physical tasks using the same multi-dimensional criteria evaluation proposed\n",
    "in the LIPS Framework;\n",
    "* Benchmark all submissions on the same environment, especially for fair speed-up comparisons.\n",
    "\n",
    "\n",
    "<b> Evaluation: </b> \n",
    "To evaluate the solutions, the competition rely on our recently proposed benchmarking framework called LIPS (Learning Industrial Physical Systems). This framework will be used to evaluate candidate solutions provided by the participants regarding significant criteria organized into 4 categories namely: ML related criteria, Physical Compliance criteria, industrial readiness and OOD generalization criteria. For each submitted solution, a global score will be computed based on the aforementioned criteria to rank it.\n",
    "\n",
    "<p style=\"text-align: center;\">\n",
    "  <img src=\"https://i.ibb.co/GsZb4bN/criteria.png\" height=\"110\" style=\"margin-right: 10px\" />\n",
    "</p>\n",
    "\n",
    "The help the development of new physics-informed ML models, the participants could use the NVIDIA MODULUS Framework, and benefit from NVIDIA support through a dedicated webinar at the beginning of the competition.\n",
    "\n",
    "The participants should train and fine-tune their models on their own computers based on the provided dataset. A cluster of 8 NVIDIA RTX A6000 GPUs will be made available by Exaion for participants who may not have their own GPU resources. For the training phase, Baseline solutions will be made available to help participants.\n",
    "\n",
    "More details on LIPS : [**LIPS paper**](https://proceedings.neurips.cc/paper_files/paper/2022/hash/b3ac9866f6333beaa7d38926101b7e1c-Abstract-Datasets_and_Benchmarks.html)\n",
    "\n",
    "LIPS [Github repository](https://github.com/IRT-SystemX/LIPS)\n",
    "\n",
    "More details about [NVIDIA SDKs & RTX GPUs](https://ml-for-physical-simulation-challenge.irt-systemx.fr/nvidia_resources/) resources\n",
    "\n",
    "## Timeline\n",
    "- Competition kick-off <b> April 16th</b>\n",
    "- Warmup phase (4 weeks) <b>April 16 avril - May 14th</b>\n",
    "- Development phase (9 weeks) <b> May 14th - July 15th </b>\n",
    "- Final phase (2 weeks) July 15th - <b> July 15th - July 29th </b>\n",
    "\n",
    "## Competition phases\n",
    "\n",
    "### This competition will run over 3 phases:\n",
    "\n",
    "- **Warm-up phase (TBD):** participants can get familiar with provided material and the competition platform, make their first submissions and provide feedback to organizers. Based on this feedback, organizers can adjust\n",
    "  and improve the competition for the next phase.\n",
    "- **Development phase (TBD):** participants will develop their solutions and will be able to test their already trained models against a provided validation dataset. They can also have access continuously to the global score corresponding the submitted solution.\n",
    "- **Final phase (TBD):** the organizers prepare the final ranking and official results. **A dedicated event will be organized to announce the winners.**\n",
    "\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "## Protocol\n",
    "\n",
    "The competition will be hosted by the [Codabench platform](https://www.codabench.org/competitions/2378/). Participants will have to:\n",
    "\n",
    "1. create an account;\n",
    "\n",
    "2. download a starting kit to prepare their submission;\n",
    "\n",
    "3. upload on the Codabench platform their trained ML models. Then, the platform will use the LIPS framework to compute scores for the submission. The score will be published on the Codabench competition page and the participant will also have access to an additional page with the detailed metrics.\n",
    "\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "## Who can take part\n",
    "\n",
    "Anyone interested in solving physical problems using ML technics is encouraged to participate in this competition. It could be a great opportunity to gather people from\n",
    "\n",
    "ML and the Scientific computing communities to leverage the synergies between these two domains.\n",
    "\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "## Prizes\n",
    "\n",
    "- 🏆 **1st Prize** : 3000 €\n",
    "- 🥈 **2nd Prize** : 2000 €\n",
    "- 🥉 **3rd Prize** : 1000 €\n",
    "- **4th Prize** : 500 €\n",
    "- **5th Prize** : 500 €\n",
    "\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "## Organizer\n",
    "A logo with the organizers will be posted here.\n",
    "\n",
    "### Organization Team:\n",
    "\n",
    "- Mouadh Yagoubi (IRT SystemX)\n",
    "- Milad Leyli-Abadi (IRT SystemX)\n",
    "- Jean-Patrick Brunet (IRT SystemX)\n",
    "- Maroua Gmati (IRT SystemX)\n",
    "- Antoine Marot (RTE)\n",
    "- Jérôme Picault (RTE)\n",
    "- Asma Farjallah (NVIDIA)\n",
    "- Marc Schoenauer (Inria)\n",
    "\n",
    "### Contact us\n",
    "Email address: ml4physim-challenge@irt-systemx.fr\n",
    "<br>\n",
    "Competition Discord channel: https://discord.gg/RZaQkHfBtg\n",
    "<br>\n",
    "Competition website: https://ml-for-physical-simulation-challenge.irt-systemx.fr/powergrid-challenge/\n",
    "\n",
    "#### The competition is hosted on CodaLab and sponsored by IRT SystemX,  RTE , NVIDIA and Exaion\n",
    "\n",
    "<br>\n",
    "<br>"
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