# Scientific Machine Learning Benchmarks arXiv: https://arxiv.org/abs/2110.12773 ยท HTML: https://mlsanity.com/paper/2110.12773/ ## Key facts - Cited by 60 papers in the map (data through 14 Oct 2026). - Builds on 5 of its 41 references that are in the map. - First posted on arXiv 25 Oct 2021. ## Builds on (most cited first, 5 of 5) - [Hamiltonian Neural Networks](https://mlsanity.com/paper/1906.01563/) (2019) - [RLBench: The Robot Learning Benchmark & Learning Environment](https://mlsanity.com/paper/1909.12271/) (2019) - [WeatherBench: A benchmark dataset for data-driven weather forecasting](https://mlsanity.com/paper/2002.00469/) (2020) - [Machine Learning and Big Scientific Data](https://mlsanity.com/paper/1910.07631/) (2019) - [A Modular Benchmarking Infrastructure for High-Performance and Reproducible Deep Learning](https://mlsanity.com/paper/1901.10183/) (2019) ## Built on by (most cited first, 30 of 60) - [Neural Operators for Accelerating Scientific Simulations and Design](https://mlsanity.com/paper/2309.15325/) (2023) - [SuperBench: A Super-Resolution Benchmark Dataset for Scientific Machine Learning](https://mlsanity.com/paper/2306.14070/) (2023) - [RealPDEBench: A Benchmark for Complex Physical Systems with Real-World Data](https://mlsanity.com/paper/2601.01829/) (2026) - [FAIR for AI: An interdisciplinary and international community building perspective](https://mlsanity.com/paper/2210.08973/) (2022) - [FastML Science Benchmarks: Accelerating Real-Time Scientific Edge Machine Learning](https://mlsanity.com/paper/2207.07958/) (2022) - [DeepSeek vs. ChatGPT vs. Claude: A Comparative Study for Scientific Computing and Scientific Machine Learning Tasks](https://mlsanity.com/paper/2502.17764/) (2025) - [Benchmarking Active Learning Strategies for Materials Optimization and Discovery](https://mlsanity.com/paper/2204.05838/) (2022) - [On the Generalization of PINNs outside the training domain and the Hyperparameters influencing it](https://mlsanity.com/paper/2302.07557/) (2023) - [Benchmark Data Repositories for Better Benchmarking](https://mlsanity.com/paper/2410.24100/) (2024) - [Towards a Benchmark for Scientific Understanding in Humans and Machines](https://mlsanity.com/paper/2304.10327/) (2023) - [Statistical Multicriteria Evaluation of LLM-Generated Text](https://mlsanity.com/paper/2506.18082/) (2025) - [Evaluating histopathology transfer learning with ChampKit](https://mlsanity.com/paper/2206.06862/) (2022) - [On the Workflows and Smells of Leaderboard Operations (LBOps): An Exploratory Study of Foundation Model Leaderboards](https://mlsanity.com/paper/2407.04065/) (2024) - [A Triumvirate of AI Driven Theoretical Discovery](https://mlsanity.com/paper/2405.19973/) (2024) - [ORCH: many analyses, one merge-a deterministic multi-agent orchestrator for discrete-choice reasoning with EMA-guided routing](https://mlsanity.com/paper/2602.01797/) (2026) - [When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development](https://mlsanity.com/paper/2209.01083/) (2022) - [Analyzing the Performance of Deep Encoder-Decoder Networks as Surrogates for a Diffusion Equation](https://mlsanity.com/paper/2302.03786/) (2023) - [Tailoring Machine Learning for Process Mining](https://mlsanity.com/paper/2306.10341/) (2023) - [Multiphysics Bench: Benchmarking and Investigating Scientific Machine Learning for Multiphysics PDEs](https://mlsanity.com/paper/2505.17575/) (2025) - [TabAttackBench: A Benchmark for Adversarial Attacks on Tabular Data](https://mlsanity.com/paper/2505.21027/) (2025) - [RDBench: ML Benchmark for Relational Databases](https://mlsanity.com/paper/2310.16837/) (2023) - [iPINNER: An Iterative Physics-Informed Neural Network with Ensemble Kalman Filter](https://mlsanity.com/paper/2506.00731/) (2025) - [AI Benchmark Democratization and Carpentry](https://mlsanity.com/paper/2512.11588/) (2025) - [SLE-FNO: Single-Layer Extensions for Task-Agnostic Continual Learning in Fourier Neural Operators](https://mlsanity.com/paper/2603.20410/) (2026) - [DropTrack -- automatic droplet tracking using deep learning for microfluidic applications](https://mlsanity.com/paper/2205.02568/) (2022) - [Probabilistic Neural Networks (PNNs) for Modeling Aleatoric Uncertainty in Scientific Machine Learning](https://mlsanity.com/paper/2402.13945/) (2024) - [Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars](https://mlsanity.com/paper/2406.17812/) (2024) - [GNN-Suite: a Graph Neural Network Benchmarking Framework for Biomedical Informatics](https://mlsanity.com/paper/2505.10711/) (2025) - [An MLCommons Scientific Benchmarks Ontology](https://mlsanity.com/paper/2511.05614/) (2025) - [SIBILA: A novel interpretable ensemble of general-purpose machine learning models applied to medical contexts](https://mlsanity.com/paper/2205.06234/) (2022) ## Abstract (arXiv) The breakthrough in Deep Learning neural networks has transformed the use of AI and machine learning technologies for the analysis of very large experimental datasets. These datasets are typically generated by large-scale experimental facilities at national laboratories. In the context of science, scientific machine learning focuses on training machines to identify patterns, trends, and anomalies to extract meaningful scientific insights from such datasets. With a new generation of experimental facilities, the rate of data generation and the scale of data volumes will increasingly require the use of more automated data analysis. At present, identifying the most appropriate machine learning algorithm for the analysis of any given scientific dataset is still a challenge for scientists. This is due to many different machine learning frameworks, computer architectures, and machine learning models. Historically, for modelling and simulation on HPC systems such problems have been addressed through benchmarking computer applications, algorithms, and architectures. Extending such a benchmarking approach and identifying metrics for the application of machine learning methods to scientific datasets is a new challenge for both scientists and computer scientists. In this paper, we describe our approach to the development of scientific machine learning benchmarks and review other approaches to benchmarking scientific machine learning.