TY - GEN
T1 - Data-Driven Modeling for Wave-Propagation
AU - van Leeuwen, Tristan
AU - van Leeuwen, Peter Jan
AU - Zhuk, Sergiy
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Many imaging modalities, such as ultrasound and radar, rely heavily on the ability to accurately model wave propagation. In most applications, the response of an object to an incident wave is recorded and the goal is to characterize the object in terms of its physical parameters (e.g., density or soundspeed). We can cast this as a joint parameter and state estimation problem. In particular, we consider the case where the inner problem of estimating the state is a weakly constrained data-assimilation problem. In this paper, we discuss a numerical method for solving this variational problem.
AB - Many imaging modalities, such as ultrasound and radar, rely heavily on the ability to accurately model wave propagation. In most applications, the response of an object to an incident wave is recorded and the goal is to characterize the object in terms of its physical parameters (e.g., density or soundspeed). We can cast this as a joint parameter and state estimation problem. In particular, we consider the case where the inner problem of estimating the state is a weakly constrained data-assimilation problem. In this paper, we discuss a numerical method for solving this variational problem.
UR - http://www.scopus.com/inward/record.url?scp=85106439803&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-55874-1_67
DO - 10.1007/978-3-030-55874-1_67
M3 - Conference contribution
AN - SCOPUS:85106439803
SN - 9783030558734
T3 - Lecture Notes in Computational Science and Engineering
SP - 683
EP - 691
BT - Numerical Mathematics and Advanced Applications, ENUMATH 2019 - European Conference
A2 - Vermolen, Fred J.
A2 - Vuik, Cornelis
PB - Springer
T2 - European Conference on Numerical Mathematics and Advanced Applications, ENUMATH 2019
Y2 - 30 September 2019 through 4 October 2019
ER -