Two heads are better than one: current landscape of integrating QSP and machine learning: an ISoP QSP SIG white paper by the working group on the integration of …
Quantitative systems pharmacology (QSP) modeling is applied to address essential
questions in drug development, such as the mechanism of action of a therapeutic agent and …
questions in drug development, such as the mechanism of action of a therapeutic agent and …
Activity trajectory generation via modeling spatiotemporal dynamics
Human daily activities, such as working, eating out, and traveling, play an essential role in
contact tracing and modeling the diffusion patterns of the COVID-19 pandemic. However …
contact tracing and modeling the diffusion patterns of the COVID-19 pandemic. However …
Learning hydrodynamic equations for active matter from particle simulations and experiments
Recent advances in high-resolution imaging techniques and particle-based simulation
methods have enabled the precise microscopic characterization of collective dynamics in …
methods have enabled the precise microscopic characterization of collective dynamics in …
[HTML][HTML] Multi-scale time-stepping of Partial Differential Equations with transformers
AP Hemmasian, AB Farimani - Computer Methods in Applied Mechanics …, 2024 - Elsevier
Developing fast surrogates for Partial Differential Equations (PDEs) will accelerate design
and optimization in almost all scientific and engineering applications. Neural networks have …
and optimization in almost all scientific and engineering applications. Neural networks have …
Differentiable physics-enabled closure modeling for Burgers' turbulence
Data-driven turbulence modeling is experiencing a surge in interest following algorithmic
and hardware developments in the data sciences. We discuss an approach using the …
and hardware developments in the data sciences. We discuss an approach using the …
Physics-informed neural ode for post-disaster mobility recovery
Urban mobility undergoes a profound decline in the aftermath of a disaster, subsequently
exhibiting a complex recovery trajectory. Effectively capturing and predicting this dynamic …
exhibiting a complex recovery trajectory. Effectively capturing and predicting this dynamic …
Heterogeneous Graph Convolutional Network‐Based Dynamic Rumor Detection on Social Media
D Yu, Y Zhou, S Zhang, C Liu - Complexity, 2022 - Wiley Online Library
The development of social media has provided open and convenient platforms for people to
express their opinions, which leads to rumors being circulated. Therefore, detecting rumors …
express their opinions, which leads to rumors being circulated. Therefore, detecting rumors …
[PDF][PDF] Autoencoder-based thermospheric density model for uncertainty quantification and real-time calibration
M Manzi, M Vasile - 8th European Conference on Space Debris, 2021 - researchgate.net
The dynamics of space objects in Low Earth Orbit (LEO) is strongly determined by the effects
of atmospheric drag: this complex interaction, dependent on the physical properties of the …
of atmospheric drag: this complex interaction, dependent on the physical properties of the …
Data-driven discovery of dynamics from time-resolved coherent scattering
Coherent X-ray scattering (CXS) techniques are capable of interrogating dynamics of nano-
to mesoscale materials systems at time scales spanning several orders of magnitude …
to mesoscale materials systems at time scales spanning several orders of magnitude …
Semi-implicit neural ordinary differential equations for learning chaotic systems
Classical neural ordinary differential equations (ODEs) trained by using explicit methods are
intrinsically constrained by stability, severely affecting their efficiency and robustness in …
intrinsically constrained by stability, severely affecting their efficiency and robustness in …