Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Lie derivatives are widely used in mathematics and physics. They are usually computed symbolically using computer algebra software. This symbolic computation might fail for very complicated expressions. Moreover, symbolic differentiation becomes more difficult if the function to be differ entiated is not described explicitly as a function but by an algorithm. This is a situation occuring quite often in modeling languages. In this contribution  present an approach for calculating Lie derivatives based on algorithmic diffe rentiation using the software package ADOL-C avoiding the drawbacks of symbolic differentiation.
['Klaus Röbenack', 'Jan Winkler', 'Siqian Wang']
I am writing a research paper on a idea LIEDRIVERS — A Toolbox for the Efficient Computation of Lie Derivatives Based on the Object-Oriented Algorithmic Differentiation Package ADOL-C
[{"from": "human", "value": "I am writing a research paper on a idea LIEDRIVERS \u2014 A Toolbox for the Efficient Computation of Lie Derivatives Based on the Object-Oriented Algorithmic Differentiation Package ADOL-C"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Lie derivatives are widely used in mathematics and physics. They are usually computed symbolically using computer algebra software. This symbolic computation might fail for very complicated expressions. Moreover, symbolic differentiation becomes more difficult if the function to be differ entiated is not described explicitly as a function but by an algorithm. This is a situation occuring quite often in modeling languages. In this contribution  present an approach for calculating Lie derivatives based on algorithmic diffe rentiation using the software package ADOL-C avoiding the drawbacks of symbolic differentiation."}]