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We present a new capability of the ice sheet model SICOPOLIS that enables flexible adjoint code generation via source transformation using the open-source algorithmic differentiation (AD) tool OpenAD. The adjoint code enables efficient calculation of sensitivities of a scalar-valued objective function or quantity of interest (QoI) to a range of important, often spatially varying model input variables, including initial and boundary conditions, as well as model parameters. Compared to earlier work on adjoint code generation of SICOPOLIS, our work is based on several important advances: (i) it is embedded within the up-to-date trunk of the SICOPOLIS repository – accounting for one and a half decades of code development and improvements – and is readily available to the wider community; (ii) the AD tool used, OpenAD, is an open-source tool; (iii) the adjoint code developed is applicable to both Greenland and Antarctica, including grounded ice as well as floating ice shelves, and with an extended choice of thermodynamical representations. A number of code refactorization steps were required. They are discussed in detail in an Appendix as they hold lessons for application of AD to legacy codes at large. As an example application, we examine the sensitivity of the total Antarctic Ice Sheet volume to changes in initial ice thickness, summer precipitation, and basal and surface temperatures across the ice sheet. Simulations of Antarctica with floating ice shelves show that over 100 years of simulation the sensitivity of total ice sheet volume to the initial ice thickness and precipitation is almost uniformly positive, while the sensitivities to surface and basal temperature are almost uniformly negative. Sensitivity to summer precipitation is largest on floating ice shelves from Queen Maud to Queen Mary Land. The largest sensitivity to initial ice thickness is at outlet glaciers around Antarctica. Comparison between total ice sheet volume sensitivities to surface and basal temperature shows that surface temperature sensitivities are higher broadly across the floating ice shelves, while basal temperature sensitivities are highest at the grounding lines of floating ice shelves and outlet glaciers. A uniformly perturbed region of East Antarctica reveals that, among the four control variables tested here, total ice sheet volume is most sensitive to variations in summer precipitation as formulated in SICOPOLIS. Comparison between adjoint- and finite-difference-derived sensitivities shows good agreement, lending confidence that the AD tool is producing correct adjoint code. The new modeling infrastructure is freely available at www.sicopolis.net under the development trunk.
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Transient Adjoint DAE Sensitivities: a Complete, Rigorous, and Numerically Accurate Formulation
Almost all practical systems rely heavily on physical parameters. As a result, parameter sensitivity, or the extent to which perturbations in parameter values affect the state of a system, is intrinsically connected to system design and optimization. We present TADsens, a method for computing the parameter sensitivities of an output of a differential algebraic equation (DAE) system. Specifically, we provide rigorous, insightful theory for adjoint sensitivity computation of DAEs, along with an efficient and numerically well-posed algorithm implemented in Berkeley MAPP. Our theory and implementation advances resolve longstanding issues that have impeded adoption of adjoint transient sensitivities in circuit simulators for over 5 decades. We present results and comparisons on two nonlinear analog circuits. TADsens is numerically well posed and accurate, and faster by a factor of 300 over direct sensitivity computation on a circuit with over 150 unknowns and 600 parameters.
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- PAR ID:
- 10334340
- Publisher / Repository:
- ASP-DAC 2022
- Date Published:
- Journal Name:
- 2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC)
- Page Range / eLocation ID:
- 513 to 518
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ASP-DAC52403.2022.9712537
