AI-ML-DL for scientific discovery
See:
- AI/Deep learning/Neural ODEs
- “AI-based conversational models and search engines” in AI/Foundation models
# Resources
- The AI revolution in science: applications and new research directions
- The AI revolution in scientific research (The Royal Society, The Alan Turing Institute)
- The AI revolution in science
- The researchers using AI to analyse peer review
# Talks
# Code
- #CODE Gt4sd-core (IBM) - GT4SD, an open-source library to accelerate hypothesis generation in the scientific discovery process
- #CODE
Deep Search
- https://ds4sd.github.io/
- https://research.ibm.com/interactive/deep-search/
- Deep Search extracts and structures data from documents in four steps: Parse, Interpret, Index, and Integrate
- Handling Scientific Articles with Deep Search
- Consensus - Ask a question, get conclusions from research papers
# Events
# References
- #PAPER Artificial intelligence in research (Musib 2017)
- #PAPER Machine learning and the physical sciences (Carleo 2019)
- #PAPER A Survey of Deep Learning for Scientific Discovery (Raghu & Schmidt, 2020) ^dlscience20
- #PAPER DeepXDE: A deep learning library for solving differential equations (Lu 2020)
- #PAPER SciANN: A Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks (Haghighat 2020)
- #PAPER Learning an Accurate Physics Simulator via Adversarial Reinforcement Learning (Jiang 2021)
- #PAPER Machine Learning for Scientific Discovery (Surana 2021)
- #PAPER Machine Intelligence for Scientific Discovery and Engineering Invention (Daniels 2021). White paper
- #PAPER Optimizing the synergy between physics and machine learning (2021). Editorial
- #PAPER Data-driven discovery of Green’s functions with human-understandable deep learning (Boulle 2022)
- #PAPER
OpenMedIA: Open-Source Medical Image Analysis Toolbox and Benchmark under Heterogeneous AI Computing Platforms (Zhuang 2022)
- #CODE https://git.openi.org.cn/OpenMedIA
- OpenMedIA is an open-source toolbox library containing a rich set of deep learning methods for medical image analysis under heterogeneous AI computing platforms
- #PAPER Leakage and the Reproducibility Crisis in ML-based Science (Kapoor 2022)
- #PAPER Journal Impact Factor and Peer Review Thoroughness and Helpfulness: A Supervised Machine Learning Study (Severin 2022)
- #PAPER Galactica: A Large Language Model for Science (Taylor 2022)
- #PAPER How Generative AI models such as ChatGPT can be (Mis)Used in SPC Practice, Education, and Research? An Exploratory Study (Megahed 2023)