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The past years have seen an explosive growth in the use and adoption of artificial intelligence (AI) and machine learning (ML) frameworks across industries and applications, driven largely by advances in deep learning architectures, increased computational power, and the availability of massive datasets for model training. Emerging foundational models such as OpenAI’s GPT series and Google’s Gemini have revolutionized natural language processing by enabling zero-shot and few-shot learning capabilities, significantly reducing the need for task-specific training. Generative models like DALL-E, Stable Diffusion, and ChatGPT have transformed not only creative industries but also scientific research by enabling on-the-fly creation of new content, such as high-quality images, text and even code. Within the field of Scientific ML (SciML), deep learning (DL) models have demonstrated utility as fast non-intrusive surrogates for expensive high-fidelity models. They have also been used to enable data-driven physics discovery.
While AI/ML have shown potential for advancing research in various science and engineering applications, applying AI/ML methods within these domains comes with some challenges, including:
The existence of open questions related to these challenges has led to some controversies related to the usage of AI/ML in science and engineering applications, prompting researchers to pose questions such as: “Is AI leading to a reproducibility crisis in science?” [1], “Can physics-informed neural networks beat the finite element method?” [2], and “What would be a precise, field-appropriate definition of what constitutes a foundational model in computational science, and do existing models claiming to be foundational models satisfy it?” [3]. There are also studies that show that "Weak baselines and reporting biases lead to overoptimism in machine learning” [4]. The goal of this special issue of CiSE is to give authors the opportunity to present their perspectives on potentially controversial topics involving the usage of AI/ML in science and engineering applications as they relate to the challenges itemized above. Authors are encouraged to include ideas on how to resolve these controversies and/or address/mitigate the challenges/open questions these controversies relate to. We welcome and anticipate a wide assortment of submissions, ranging from research papers describing new methods/frameworks in the context of this special issue’s theme, to survey papers evaluating/comparing existing methods, to higher-level evidence-based position papers.
For author information and guidelines on submission criteria, visit the Author’s Information page. Articles should be between 2,400 and 6,250 words, including all main body, abstract, keyword, bibliography (25 references or less), and biography text. Each table and figure counts for 250 words.
Please submit papers through the IEEE Author Portal and be sure to select the special issue or special section name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts. If requested, abstracts should be sent by email to the guest editors directly.
In addition to submitting your paper to CiSE, you are also encouraged to upload the data related to your paper to IEEE DataPort. IEEE DataPort is IEEE's data platform that supports the storage and publishing of datasets while also providing access to thousands of research datasets. Uploading your dataset to IEEE DataPort will strengthen your paper and will support research reproducibility. Your paper and the dataset can be linked, providing a good opportunity for you to increase the number of citations you receive. Data can be uploaded to IEEE DataPort prior to submitting your paper or concurrent with the paper submission. Thank you!
We strongly encourage submissions that include multimedia and/or data, which will be featured online. If your submission includes such materials, please let the special issue editors know at:
