Development of a Nanostructured Lipid Carrier (NLC) by a Low-Energy Method, Comparison of Release Kinetics and Molecular Dynamics Simulation. [PDF]
Pharmaceutics, 2021 Lipid nanocarriers have a great potential for improving the physicochemical characteristics and behavior of poorly water-soluble drugs, such as aqueous dispersibility and oral bioavailability.
Ortiz AC +3 more
europepmc +2 more sources
A highly accurate metadynamics-based Dissociation Free Energy method to calculate protein-protein and protein-ligand binding potencies. [PDF]
Sci Rep, 2022 Although seeking to develop a general and accurate binding free energy calculation method for protein–protein and protein–ligand interactions has been a continuous effort for decades, only limited successes have been obtained so far.
Wang J +4 more
europepmc +2 more sources
Energy-Efficient Wireless Sensor Network with an Unequal Clustering Protocol Based on a Balanced Energy Method (EEUCB). [PDF]
Sensors (Basel), 2021 A hot spot problem is a problem where cluster nodes near to the base station (BS) tend to drain their energy much faster than other nodes due to the need to perform more communication.
Jasim AA +5 more
europepmc +2 more sources
A deep learning energy method for hyperelasticity and viscoelasticity [PDF]
European Journal of Mechanics - A/Solids, 2022 The potential energy formulation and deep learning are merged to solve partial differential equations governing the deformation in hyperelastic and viscoelastic materials. The presented deep energy method (DEM) is self-contained and meshfree.
D. Abueidda +5 more
semanticscholar +1 more source
The mixed deep energy method for resolving concentration features in finite strain hyperelasticity [PDF]
Journal of Computational Physics, 2021 The introduction of Physics-informed Neural Networks (PINNs) has led to an increased interest in deep neural networks as universal approximators of PDEs in the solid mechanics community. Recently, the Deep Energy Method (DEM) has been proposed.
J. Fuhg, N. Bouklas
semanticscholar +1 more source
Deep energy method in topology optimization applications [PDF]
Acta Mechanica, 2022 This paper explores the possibilities of applying physics-informed neural networks (PINNs) in topology optimization (TO) by introducing a fully self-supervised TO framework based on PINNs.
Junyan He +5 more
semanticscholar +1 more source
On the use of graph neural networks and shape‐function‐based gradient computation in the deep energy method [PDF]
International Journal for Numerical Methods in Engineering, 2022 A graph convolutional network (GCN) is employed in the deep energy method (DEM) model to solve the momentum balance equation in three‐dimensional space for the deformation of linear elastic and hyperelastic materials due to its ability to handle ...
Junyan He +3 more
semanticscholar +1 more source
CENN: Conservative energy method based on neural networks with subdomains for solving variational problems involving heterogeneous and complex geometries [PDF]
Computer Methods in Applied Mechanics and Engineering, 2021 We propose a conservative energy method based on neural networks with subdomains for solving variational problems (CENN), where the admissible function satisfying the essential boundary condition without boundary penalty is constructed by the radial ...
Yi-Zhou Wang +4 more
semanticscholar +1 more source
A GPU-Accelerated Parameter Interpolation Thermodynamic Integration Free Energy Method. [PDF]
J Chem Theory Comput, 2018 There has been a resurgence of interest in free energy methods motivated by the performance enhancements offered by molecular dynamics (MD) software written for specialized hardware, such as graphics processing units (GPUs).
Giese TJ, York DM.
europepmc +2 more sources
Improving the accuracy of the deep energy method
Acta Mechanica, 2023 The deep energy method (DEM), a type of physics-informed neural network, is evolving as an alternative to finite element analysis. It employs the principle of minimum potential energy to predict an object’s behavior under various boundary conditions ...
Charul Chadha +5 more
semanticscholar +1 more source