Potential Of Artificial Neural Networks Research Articles

GPU-accelerated artificial neural network potential for molecular dynamics simulation

Artificial neural network potential (ANNP), obtained by training a large database by the first-principles calculations, has become popular in molecular dynamics (MD) simulation since it can capture accurate physical and chemical properties. However, the complex procedure and heavy data dependence during implementation make the performance of CPU-only runs worse, which further limits its application. In this contribution, we report a flexible computation method for ANNP in LAMMPS, in which the simulation box is divided into several parts in accordance with the resource on the accelerator such as the size of global memory and the number of work items (cores). The number of dividing parts has little influence on the performance when the number of calculated atoms per loop is larger than the number of work items on the device. In this approach, the forces of neighbor atoms are updated using hierarchical memory without atomic operation. Typical dynamic and static tests are performed to validate the implementation. The results show that our approach is 12 or 13 times faster when using one graphics processing unit (GPU) compared with 8-MPI tasks CPU-only runs. Additionally, this implementation is supported for CUDA- and OpenCL-enabled GPU cards.

Models for estimating evapotranspiration using artificial neural networks, and their physical interpretation

AbstractEstimation of evapotranspiration (ET) requires a knowledge of the values of many climatic variables, some of which require special equipment and careful observations. Although ET is an important component of water balance, the data required for its accurate estimation are commonly available only at widely spaced measurement stations. The major objective of this study was to estimate ET using an artificial neural network (ANN) technique and to examine if a trained neural network with limited input variables can estimate ET efficiently. The results indicate that even with limited climatic variables an ANN can estimate ET accurately. The paper also outlines a procedure to evaluate the effects of input variables on the output variable using the weight connections of ANN models. Such an analysis performed on the ANN‐ET models developed was able to explain the reasons for the ANN's potential in estimating the ET effectively from limited climatic data. Copyright © 2008 John Wiley & Sons, Ltd.