Nadipuram R. Prasad¹, Salvador Almanza-Garcia¹, Thomas T. Lu²

CMC-Computers, Materials & Continua, Vol.14, No.1, pp. 1-22, 2009, DOI:10.3970/cmc.2009.014.001

Abstract The paper presents a revolutionary framework for the modeling, detection, characterization, identification, and machine-learning of anomalous behavior in observed phenomena arising from a large class of unknown and uncertain dynamical systems. An evolved behavior would in general be very difficult to correct unless the specific anomalous event that caused such behavior can be detected early, and any consequence attributed to the specific anomaly following its detection. Substantial investigative time and effort is required to back-track the cause for abnormal behavior and to recreate the event sequence leading to such abnormal behavior. The need to automatically detect anomalous behavior is therefore… More >

B.B. Karki¹, D. Bhattarai¹, L. Stixrude²

CMC-Computers, Materials & Continua, Vol.3, No.3, pp. 107-118, 2006, DOI:10.3970/cmc.2006.003.107

Abstract Computer modeling of liquid phase poses tremendous challenge: It requires a relatively large simulation size, long simulation time and accurate interatomic interaction and as such, it produces massive amounts of data. Recent advances in hardware and software have made it possible to accurately simulate the liquid phase. This paper reports the details of methodology used in the context of liquid simulations and subsequent analysis of the output data. For illustration purpose, we consider the results for the liquid phases of two geophysically relevant materials, namely MgO and MgSiO₃. The simulations are performed using the parallel first-principles molecular dynamics (FPMD) technique… More >