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您的检索式：作者名="Danial Khatamsaz"
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| 1 | Bayesian optimization with active learning of design constraints using an entropy-based approach显示文摘The design of alloys for use in gas turbine engine blades is a complex task that involves balancing multiple objectives and constraints.Candidate alloys must be ductile at room temperature and retain their yield strength at high temperatures,as well as possess low density,high thermal conductivity,narrow solidification range,high solidus temperature,and a small linear thermal expansion coefficient.Traditional Integrated Computational Materials Engineering(ICME)methods are not sufficient for exploring combinatorially-vast alloy design spaces,optimizing for multiple objectives,nor ensuring that multiple constraints are met.In this work,we propose an approach for solving a constrained multi-objective materials design problem over a large composition space,specifically focusing on the Mo-Nb-Ti-V-W system as a representative Multi-Principal Element Alloy(MPEA)for potential use in next-generation gas turbine blades.Our approach is able to learn and adapt to unknown constraints in the design space,making decisions about the best course of action at each stage of the process.As a result,we identify 21 Pareto-optimal alloys that satisfy all constraints.Our proposed framework is significantly more efficient and faster than a brute force approach. | Danial Khatamsaz Brent Vela Prashant Singh Duane D.Johnson Douglas Allaire Raymundo Arróyave | 2023 | npj Computational Materials2023，，1： | 1 |
| 2 | A physics informed bayesian optimization approach for material design: application to NiTi shape memory alloys显示文摘The design of materials and identification of optimal processing parameters constitute a complex and challenging task,necessitating efficient utilization of available data.Bayesian Optimization(BO)has gained popularity in materials design due to its ability to work with minimal data.However,many BO-based frameworks predominantly rely on statistical information,in the form of input-output data,and assume black-box objective functions.In practice,designers often possess knowledge of the underlying physical laws governing a material system,rendering the objective function not entirely black-box,as some information is partially observable.In this study,we propose a physics-informed BO approach that integrates physics-infused kernels to effectively leverage both statistical and physical information in the decision-making process.We demonstrate that this method significantly improves decision-making efficiency and enables more data-efficient BO.The applicability of this approach is showcased through the design of NiTi shape memory alloys,where the optimal processing parameters are identified to maximize the transformation temperature. | Danial Khatamsaz Raymond Neuberger Arunabha M.Roy Sina Hossein Zadeh Richard Otis Raymundo Arróyave | 2023 | npj Computational Materials2023，，1： | 0 |
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