Parameter identification for wildfire forecasting models often relies on case-by-case tuning or posterior diagnosis/analysis, which can be computationally expensive due to the complexity of forward prediction model. In this paper, we introduce an efficient parameter flexible fire algorithm based machine learning and reduced order modelling techniques. Using a training dataset generated by physi...

The identification of production functions from data is an important task in the modelling of economic growth. In this paper, we consider a non-parametric approach to this identification problem in the context of the spatial Solow model which allows for rather general production functions, in particular convex-concave ones that have recently been proposed as reasonable shapes. We formulate the ...

This paper gives an overview of the use of inverse methods in damage detection and location, using measured vibration data. Inverse problems require the use of a model and the identification of uncertain parameters of this model. Damage is often local in nature and although the effect of the loss of stiffness may require only a small number of parameters, the lack of knowledge of the location m...

In this paper, a simple and convenient method – Recursive Matrix method – is proposed for kinematic and dynamic analysis of all types of complex manipulators. After addressing the principle of the method, an example – a 3-DOF parallel manipulator with prismatic actuators – is demonstrated for the efficiency of the method in solving kinematic and dynamic problems of complex manipulators. With th...

This paper presents the software package SYMORO+ for the automatic symbolic modelling of robots. This package permits to generate the direct geometric model, the inverse geometric model, the direct kinematic model, the inverse kinematic model, the dynamic model, and the inertial parameters identification models. The structure of the robots can be serial, tree structure or containing closed loop...

Reverse engineering of interrupt-driven real-time programs with timing constraints is a particularly challenging research area, because the functional behaviour of a program, and the non-functional timing requirements, are implicit and can be very difficult to discover. However, in this paper we present a significant advance in this area, which is achieved by modelling real-time programs with i...

Autoregressive models are among the best performing neural density estimators. We describe an approach for increasing the flexibility of an autoregressive model, based on modelling the random numbers that the model uses internally when generating data. By constructing a stack of autoregressive models, each modelling the random numbers of the next model in the stack, we obtain a type of normaliz...