Operational Semantics for a Modular Equation Language

Höger, Christoph

doi:10.3384/ecp13090002

Cited by 2 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The closest work to ours is the work by Höger in [37][38][39]. In particular, in [37], the author explores the modular semantics of a non-causal language using automatic higherorder differentiation. The technique for performing automatic differentiation in this work is derived from [40].…”

Section: Related Workmentioning

confidence: 99%

“…Indeed, although Faà di Bruno's formula has been extended to the multivariate case [43], it is unclear whether an implementation that use it can be made efficient. We would be interested to study other approaches to higher-order automatic differentiation (e.g., [40], used in [37]) to see if they provide solutions to this problem. Supporting user-defined signal functions could also allow for some integration of reactive causal programming, in particular coming from Functional Reactive Programming, in FHM's implementations.…”

Section: External Functionsmentioning

confidence: 99%

See 1 more Smart Citation

Modular Compilation for a Hybrid Non-Causal Modelling Language

Chupin

Nilsson

2021

Electronics

View full text Add to dashboard Cite

Non-causal modelling is a powerful approach to modelling physical systems in a variety of domains from science and engineering. Non-causal modelling languages enable a high-level and modular approach to modelling. However, it is hard to compile non-causal languages modularly (in the sense of separate compilation). This causes difficulties when simulating large models for which code generation takes a long time, or structurally singular models in which parts of the model are allowed to change at runtime. In this work, we introduce a technique we call order-parametric differentiation to allow truly modular compilation. The idea is to generate (machine) code that can compute derivatives of any order of an expression as needed, thus allowing for ahead-of-time modular compilation of a hybrid non-causal language. We also develop a compilation scheme that enables using partial models as first-class objects in a seamless way and simulating them without the need for just-in-time compilation, even in the presence of structural dynamism. We present a performance evaluation of the scheme we used and study its shortcomings and possible improvements, demonstrating that it is a feasible complement to existing implementation techniques for cases where true modular compilation is a primary objective.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: External Functionsmentioning

confidence: 99%

Modular Compilation for a Hybrid Non-Causal Modelling Language

Chupin

Nilsson

2021

Electronics

View full text Add to dashboard Cite

show abstract

“…However, the above function can be generalized for more complicated input languages using techniques like automatic differentiation (Höger (2013)). Hence we conjecture that differentiation is possible for all quasilinear equations derived from all MODELICA equations.…”

Section: Derivatives and Hidden Constraintsmentioning

confidence: 99%

Internalized State-Selection: Generation and Integration of Quasi-Linear Differential-Algebraic Equations

Höger

Steinbrecher

2015

Linköping Electronic Conference Proceedings

Self Cite

View full text Add to dashboard Cite

In modeling and simulation of dynamical processes frequently higher index differential-algebraic equations (DAEs) arise. Since an attempt to solve higher-index DAEs directly yields several numerical problems, a regularization in combination with a robust and efficient integration is required. QUALIDAES is a DAE solver designed to make explicit use of such a regularization. It allows for the solution of over-determined quasi-linear DAEs of the form M(x,t)ẋ = f (x,t), 0 = g(x,t). Such DAEs arise naturally if a quasi-linear DAE is regularized by augmentation with the set of its (hidden) constraints. General DAEs can be brought into the quasi-linear form. To this end, equations can be transformed into the specific input format expected by QUALIDAES. This transformation can be implemented in a functional style and yields a non-trivial result. Additionally it provides an onthe-fly solution for the occurrence of higher-order derivatives.

show abstract

Operational Semantics for a Modular Equation Language

Cited by 2 publications

References 11 publications

Modular Compilation for a Hybrid Non-Causal Modelling Language

Modular Compilation for a Hybrid Non-Causal Modelling Language

Internalized State-Selection: Generation and Integration of Quasi-Linear Differential-Algebraic Equations

Contact Info

Product

Resources

About