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10篇 您的检索式：作者名="Peter Benner"

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		题名	作者	年代	出处	被引量
	1	Immune Response Against Frameshift-Induced Neopeptides in HNPCC Patients and Healthy HNPCC Mutation Carriers显示文摘	Yvette Schwitalle Matthias Kloor Susanne Eiermann Michael Linnebacher Peter Kienle Hanns Peter Knaebel Mirjam Tariverdian Axel Benner Magnus von Knebel Doeberitz	2008	Gastroenterology2008，，4：	1
	2	A Meta - Analysis of School -based Social Skills Interventions for Children with Autism Spectrum Disorders 显示文摘	Bellini S Peters J K Benner L	2007	Remedial and Special Education2007，，3：	1
	3	Squamous metaplasia of the bronchial mucosa and its relationship to smoking显示文摘	Peters E J Morice R Benner SE	1993	Chest1993，103，5：	1
	4	Solving stable generalized Lyapunov equations with the matrix sign function显示文摘	Peter Benner Enrique S. Quintana-Ortí	1999	Numerical Algorithms1999，，1：	1
	5	An exact line search method for solving generalized continuous-time algebraic Riccati equations显示文摘	Benner Peter Byers Ralph	1998	IEEE Transactions on Automatic Control1998，43，1：	1
	6	Bench Marks for the Numerical Solution of Algebratic Riccati Equations 显示文摘	Peter Benner Alan J Laub Volker Mehrmann	1997	IEEE Control Systems1997，，：	1
	7	A numerically stable, structure preserving method for computing the eigenvalues of real Hamiltonian or symplectic pencils显示文摘	Peter Benner Volker Mehrmann Hongguo Xu	1998	Numerische Mathematik1998，，3：	1
	8	A numerically stable, structure preserving method for computing the eigenvalues of real Hamiltonian or symplectic pencils显示文摘	Peter Benner Volker Mehrmann Hongguo Xu	1998	Numerische Mathematik1998，，3：	1
	9	State-space truncation methods for parallel model reduction of large reduction of largescale systems显示文摘	Peter Benner Quitana	2003	Parallel Computing2003，29，11：	1
	10	An artificial neural network for surrogate modeling of stress fields in viscoplastic polycrystalline materials显示文摘The purpose of this work is the development of a trained artificial neural network for surrogate modeling of the mechanical response of elasto-viscoplastic grain microstructures.To this end,a U-Net-based convolutional neural network(CNN)is trained using results for the von Mises stress field from the numerical solution of initial-boundary-value problems(IBVPs)for mechanical equilibrium in such microstructures subject to quasi-static uniaxial extension.The resulting trained CNN(tCNN)accurately reproduces the von Mises stress field about 500 times faster than numerical solutions of the corresponding IBVP based on spectral methods.Application of the tCNN to test cases based on microstructure morphologies and boundary conditions not contained in the training dataset is also investigated and discussed.	Mohammad S.Khorrami Jaber R.Mianroodi Nima H.Siboni Pawan Goyal Bob Svendsen Peter Benner Dierk Raabe	2023	npj Computational Materials2023，，1：	0

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