Surrogate model–based inverse parameter estimation in deep drawing using automatic knowledge acquisition

CH4 Parameter Estimation in CLM4.5bgc Using Surrogate Global Optimization

CH4 Parameter Estimation in CLM4.5bgc Using Surrogate Global Optimization J. Müller1, R. Paudel2, N. Mahowald2, and C.A. Shoemaker3 1Center for Computational Sciences and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720 2Earth and Atmospheric Sciences, Cornell University, Ithaca, NY, 14853 3School of Civil and Environmental Engineering, Cornell University, Ithaca, NY, 148...

Automatic Medical Knowledge Acquisition Using Question-Answering

We aim at proposing a rule generation approach to automatically acquire structured rules that can be used in decision support systems for drug prescription. We apply a question-answering engine to answer specific information requests. The rule generation is seen as an equation problem, where the factors are known items of the rule (e.g., an infectious disease, caused by a given bacteria) and so...

Parameter Estimation and Inverse Problems

System Parameter Estimation in Tomographic Inverse Problems

Inverse problems are typically solved under the assumption of known geometric system parameters describing the forward problem. Should such information be unavailable or inexact, the estimation of these parameters from only observed sensor data may be necessary prior to reconstruction of the desired signal. We demonstrate the feasibility of such estimation via maximum-likelihood methods for the...

Degradation in Spatial Knowledge Acquisition When Using Automatic Navigation Systems

Over-reliance on automated navigation systems may cause users to be “mindless” of the environment and not develop the spatial knowledge that maybe required when automation fails. This research focused on the potential degradation in spatial knowledge acquisition due to the reliance on automatic wayfinding systems. In addition, the impact of “keeping the user in the loop” strategies on spatial k...