Overview

Scientific knowledge-guided machine learning (KGML) is an emerging field of research where scientific knowledge is deeply integrated in ML frameworks to produce solutions that are scientifically grounded, explainable, and likely to generalize on out-of-distribution samples even with limited training data. By using both scientific knowledge and data as complementary sources of information in the design, training, and evaluation of ML models, KGML seeks a distinct departure from black-box data-only methods and holds great potential for accelerating scientific discovery in a number of disciplines.

The major goal of this workshop is to explore the depth and diversity of research methodologies being explored in KGML for a wide range of scientific applications. Through this workshop, we also aim to expose gaps in the current state of KGML research providing novel opportunities to advance AI foundations while accelerating discoveries in problems of high societal relevance.

The KGML2024 workshop is a continuation of previous workshops (KGML2020 and KGML2021) held as part of a project funded by the NSF's Harnessing the Data Revolution (HDR) program as well as annual workshops held at the AAAI Fall symposium series during Fall 2020, 2021, and 2022 and the KGML Bridge Program at AAAI 2024. See the KGML book and a recent perspective article for a coverage of research topics in KGML.