TGM-Nets: A deep learning framework for enhanced forecasting of tumor growth by integrating imaging and modeling
Prediction and uncertainty quantification of tumor progression are vital in clinical practice, ie,
disease prognosis and decision-making on treatment strategies. In this work, we propose …
disease prognosis and decision-making on treatment strategies. In this work, we propose …
Efficient error certification for physics-informed neural networks
Recent work provides promising evidence that Physics-Informed Neural Networks (PINN)
can efficiently solve partial differential equations (PDE). However, previous works have …
can efficiently solve partial differential equations (PDE). However, previous works have …
A deep neural network for operator learning enhanced by attention and gating mechanisms for long-time forecasting of tumor growth
Forecasting tumor progression and assessing the uncertainty of predictions play a crucial
role in clinical settings, especially for determining disease outlook and making informed …
role in clinical settings, especially for determining disease outlook and making informed …
Deepphysinet: Bridging deep learning and atmospheric physics for accurate and continuous weather modeling
Accurate weather forecasting holds significant importance to human activities. Currently,
there are two paradigms for weather forecasting: Numerical Weather Prediction (NWP) and …
there are two paradigms for weather forecasting: Numerical Weather Prediction (NWP) and …
Geometry-informed neural networks
Geometry is a ubiquitous tool in computer graphics, design, and engineering. However, the
lack of large shape datasets limits the application of state-of-the-art supervised learning …
lack of large shape datasets limits the application of state-of-the-art supervised learning …
Quantifying local and global mass balance errors in physics-informed neural networks
Physics-informed neural networks (PINN) have recently become attractive for solving partial
differential equations (PDEs) that describe physics laws. By including PDE-based loss …
differential equations (PDEs) that describe physics laws. By including PDE-based loss …
Protein‐Based Patterning to Spatially Functionalize Biomimetic Membranes
M Reverte‐López, S Gavrilovic… - Small …, 2023 - Wiley Online Library
The bottom‐up reconstitution of proteins for their modular engineering into synthetic cellular
systems can reveal hidden protein functions in vitro. This is particularly evident for the …
systems can reveal hidden protein functions in vitro. This is particularly evident for the …
Enhancing computational accuracy in surrogate modeling for elastic–plastic problems by coupling S-FEM and physics-informed deep learning
M Zhou, G Mei, N Xu - Mathematics, 2023 - mdpi.com
Physics-informed neural networks (PINNs) provide a new approach to solving partial
differential equations (PDEs), while the properties of coupled physical laws present potential …
differential equations (PDEs), while the properties of coupled physical laws present potential …
An Interpretable Approach to the Solutions of High-Dimensional Partial Differential Equations
In recent years, machine learning algorithms, especially deep learning, have shown
promising prospects in solving partial differential equations (PDEs). However, as the …
promising prospects in solving partial differential equations (PDEs). However, as the …
Multi-physics modeling and finite-element formulation of neuronal dendrite growth with electrical polarization
The neuron serves as the basic computational unit for the brain. Altered neuronal
morphologies are usually found in various neurological diseases, such as Down syndrome …
morphologies are usually found in various neurological diseases, such as Down syndrome …