When engineers at Sumitomo Riko needed to speed up the design cycle for automotive rubber and polymer components, they turned ...

Applying a few simplifying assumptions and constructing a set of ordinary differential equations leads to a model that ...

International Business Machines Corporation (NYSE:IBM) and ETH Zurich said they are entering a 10-year partnership to develop ...

When Apple discovers trending popular emojis, or when Google reports traffic at a busy restaurant, they're analyzing large ...

In this tutorial, we explore how to solve differential equations and build neural differential equation models using the Diffrax library. We begin by setting up a clean computational environment and ...

Physics-aware machine learning integrates domain-specific physical knowledge into machine learning models, leading to the development of physics-informed neural networks (PINNs). PINNs embed physical ...

Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential ...

ABSTRACT: Human T-cell leukemia/lymphoma virus type I (HTLV-I) is a pathogenic retrovirus, and cytotoxic T lymphocytes (CTLs) or specific CD8+ cytotoxic T lymphocytes are considered crucial factors in ...

Microbial communities drive essential biological processes across ecosystems, yet predicting their dynamics and functions remains challenging due to context-dependent interactions. We develop a ...

Abstract: We investigate the feasibility of deriving surrogate ordinary differential equations (ODEs) from individual-based models (IBMs) simulated in the PhysiCell platform. As a preliminary ...

Abstract: This paper introduces Physics-Informed Deep Equilibrium Models (PIDEQs) for solving initial value problems (IVPs) of ordinary differential equations (ODEs). Leveraging recent advancements in ...