Modeling the interactions of water and energy systems is important to the enforcement of infrastructure security and system sustainabil-ity.To this end,recent technological advancement has allowed the production of la...Modeling the interactions of water and energy systems is important to the enforcement of infrastructure security and system sustainabil-ity.To this end,recent technological advancement has allowed the production of large volumes of data associated with functioning of these sectors.We are beginning to see that statistical and machine learning techniques can help elucidate characteristic patterns across these systems from water availability,transport,and use to energy generation,fuel supply,and customer demand,and in the interde-pendencies among these systems that can leave these systems vul-nerable to cascading impacts from single disruptions.In this paper,we discuss ways in which data and machine learning can be applied to the challenges facing the energy-water nexus along with the potential issues associated with the machine learning techniques themselves.We then survey machine learning techniques that have found application to date in energy-water nexus problems.We con-clude by outlining future research directions and opportunities for collaboration among the energy-water nexus and machine learning communities that can lead to mutual synergistic advantage.展开更多
Recent technical advances in the area of nanoscale imaging,spectroscopy and scattering/diffraction have led to unprecedented capabilities for investigating materials structural,dynamical and functional characteristics...Recent technical advances in the area of nanoscale imaging,spectroscopy and scattering/diffraction have led to unprecedented capabilities for investigating materials structural,dynamical and functional characteristics.In addition,recent advances in computational algorithms and computer capacities that are orders of magnitude larger/faster have enabled large-scale simulations of materials properties starting with nothing but the identity of the atomic species and the basic principles of quantum and statistical mechanics and thermodynamics.Along with these advances,an explosion of high-resolution data has emerged.This confluence of capabilities and rise of big data offer grand opportunities for advancing materials sciences but also introduce several challenges.In this perspective,we identify challenges impeding progress towards advancing materials by design(e.g.,the design/discovery of materials with improved properties/performance),possible solutions and provide examples of scientific issues that can be addressed using a tightly integrated approach where theory and experiments are linked through big-deep data.展开更多
基金This manuscript has been authored by employees of UT- Battelle, under contract DE AC05-000R22725 with the US Department of Energy. The authors would also like to acknowledge thefinancial and intellectual support for this research by the Integrated Assessment Research Programof the US Department of Energy's Office of Science, Biological and Environmental Research. Thiswork is supported in part by NSF ACI-1541215.
文摘Modeling the interactions of water and energy systems is important to the enforcement of infrastructure security and system sustainabil-ity.To this end,recent technological advancement has allowed the production of large volumes of data associated with functioning of these sectors.We are beginning to see that statistical and machine learning techniques can help elucidate characteristic patterns across these systems from water availability,transport,and use to energy generation,fuel supply,and customer demand,and in the interde-pendencies among these systems that can leave these systems vul-nerable to cascading impacts from single disruptions.In this paper,we discuss ways in which data and machine learning can be applied to the challenges facing the energy-water nexus along with the potential issues associated with the machine learning techniques themselves.We then survey machine learning techniques that have found application to date in energy-water nexus problems.We con-clude by outlining future research directions and opportunities for collaboration among the energy-water nexus and machine learning communities that can lead to mutual synergistic advantage.
基金sponsored by the Division of Materials Sciences and Engineering,BES,DOE(RKV and SVK).
文摘Recent technical advances in the area of nanoscale imaging,spectroscopy and scattering/diffraction have led to unprecedented capabilities for investigating materials structural,dynamical and functional characteristics.In addition,recent advances in computational algorithms and computer capacities that are orders of magnitude larger/faster have enabled large-scale simulations of materials properties starting with nothing but the identity of the atomic species and the basic principles of quantum and statistical mechanics and thermodynamics.Along with these advances,an explosion of high-resolution data has emerged.This confluence of capabilities and rise of big data offer grand opportunities for advancing materials sciences but also introduce several challenges.In this perspective,we identify challenges impeding progress towards advancing materials by design(e.g.,the design/discovery of materials with improved properties/performance),possible solutions and provide examples of scientific issues that can be addressed using a tightly integrated approach where theory and experiments are linked through big-deep data.
