The generalized thermo-elasticity theory, i.e., Green and Naghdi （G-N） Ⅲ theory, with energy dissipation （TEWED） is employed in the study of time-harmonic plane wave propagation in an unbounded, perfectly electri...The generalized thermo-elasticity theory, i.e., Green and Naghdi （G-N） Ⅲ theory, with energy dissipation （TEWED） is employed in the study of time-harmonic plane wave propagation in an unbounded, perfectly electrically conducting elastic medium subject to primary uniform magnetic field. A more general dispersion equation with com- plex coefficients is obtained for coupled magneto-thermo-elastic wave solved in complex domain by using the Leguerre＇s method. It reveals that the coupled magneto-thermoelastic wave corresponds to modified dilatational and thermal wave propagation with finite speeds modified by finite thermal wave speeds, thermo-elastic coupling, thermal diffusivity, and the external magnetic field. Numerical results for a copper-like material are presented.展开更多
With the growing availability of data within various scientific domains,generative models hold enormous potential to accelerate scientific discovery.They harness powerful representations learned from datasets to speed...With the growing availability of data within various scientific domains,generative models hold enormous potential to accelerate scientific discovery.They harness powerful representations learned from datasets to speed up the formulation of novel hypotheses with the potential to impact material discovery broadly.We present the Generative Toolkit for Scientific Discovery(GT4SD).This extensible open-source library enables scientists,developers,and researchers to train and use state-of-the-art generative models to accelerate scientific discovery focused on organic material design.展开更多
Surrogate models for partial differential equations are widely used in the design of metamaterials to rapidly evaluate the behavior of composable components.However,the training cost of accurate surrogates by machine ...Surrogate models for partial differential equations are widely used in the design of metamaterials to rapidly evaluate the behavior of composable components.However,the training cost of accurate surrogates by machine learning can rapidly increase with the number of variables.For photonic-device models,we find that this training becomes especially challenging as design regions grow larger than the optical wavelength.We present an active-learning algorithm that reduces the number of simulations required by more than an order of magnitude for an NN surrogate model of optical-surface components compared to uniform random samples.Results show that the surrogate evaluation is over two orders of magnitude faster than a direct solve,and we demonstrate how this can be exploited to accelerate large-scale engineering optimization.展开更多
文摘The generalized thermo-elasticity theory, i.e., Green and Naghdi （G-N） Ⅲ theory, with energy dissipation （TEWED） is employed in the study of time-harmonic plane wave propagation in an unbounded, perfectly electrically conducting elastic medium subject to primary uniform magnetic field. A more general dispersion equation with com- plex coefficients is obtained for coupled magneto-thermo-elastic wave solved in complex domain by using the Leguerre＇s method. It reveals that the coupled magneto-thermoelastic wave corresponds to modified dilatational and thermal wave propagation with finite speeds modified by finite thermal wave speeds, thermo-elastic coupling, thermal diffusivity, and the external magnetic field. Numerical results for a copper-like material are presented.
基金The authors acknowledge Helena Montenegro,Yoel Shoshan,Nicolai Ree,Miruna Cretu and Helder Lopes for their open-source contributions to the GT4SD.
文摘With the growing availability of data within various scientific domains,generative models hold enormous potential to accelerate scientific discovery.They harness powerful representations learned from datasets to speed up the formulation of novel hypotheses with the potential to impact material discovery broadly.We present the Generative Toolkit for Scientific Discovery(GT4SD).This extensible open-source library enables scientists,developers,and researchers to train and use state-of-the-art generative models to accelerate scientific discovery focused on organic material design.
基金This work was supported in part by IBM Research,the MIT-IBM Watson AI Laboratory,the U.S.Army Research Office through the Institute for Soldier Nanotechnologies(under award W911NF-13-D-0001)by the PAPPA program of DARPA MTO(under award HR0011-20-90016).
文摘Surrogate models for partial differential equations are widely used in the design of metamaterials to rapidly evaluate the behavior of composable components.However,the training cost of accurate surrogates by machine learning can rapidly increase with the number of variables.For photonic-device models,we find that this training becomes especially challenging as design regions grow larger than the optical wavelength.We present an active-learning algorithm that reduces the number of simulations required by more than an order of magnitude for an NN surrogate model of optical-surface components compared to uniform random samples.Results show that the surrogate evaluation is over two orders of magnitude faster than a direct solve,and we demonstrate how this can be exploited to accelerate large-scale engineering optimization.
