Over the past decade, the integration of Artificial Intelligence (AI) for scientific exploration has grown as a transformative force, propelling research into new realms of discovery. The AI4DifferentialEquations in Science workshop at ICLR 2024 invites participants on a dynamic journey at the interface of machine learning and computational sciences known as Scientific Machine Learning (SciML).This workshop aims to unleash innovative approaches that harness the power of AI algorithms combined with computational mathematics to advance scientific discovery and problem solving. This enables us to push the boundaries of scientific computing beyond its traditional limits. Our goal is to delve into the latest AI advancements, particularly those that significantly enhance the efficiency of solving ordinary and partial differential equations (PDEs). These methods result in significant performance gains, which allow for solutions at high resolution that were previously unfeasible or required large amounts of computation. The AI4DifferentialEquations in Science workshop aims to unlock the full potential of data-driven approaches in advancing scientific frontiers in earth sciences, climate and computational fluid dynamics to name a few.Key topics include but are not limited to:- Exploration of novel applications of deep learning techniques in scientific simulations of partial or ordinary differential equations.- Forward and inverse problems in PDEs to equation discovery, design optimization, and beyond, to witness the diverse applications of AI in scientific pursuits. - Explainability and interpretability of AI models in scientific contexts.The AI4DifferentialEquations in Science workshop will be an interactive event for researchers and practitioners at various stages of their careers with diverse backgrounds to come together and share their perspectives and experiences on leveraging AI techniques for their particular science problems. We aim to better bridge the gaps between the different fields of numerical analysis, machine learning and the sciences by sharing insights, methodologies, and challenges in harnessing AI's power for the greater good of scientific exploration.