Software Tool for Acausal Physical Modelling and Simulation

Modelling and simulation are key tools for analysis and design of systems and processes from almost any scientific or engineering discipline. Models of complex systems are typically built on acausal Differential-Algebraic Equations (DAE) and discrete events using Object-Oriented Modelling (OOM) languages, and some of their key concepts can be explained as symmetries. To obtain a computer executable version from the original model, several algorithms, based on bipartite symmetric graphs, must be applied for automatic equation generation, removing alias equations, computational causality assignment, equation sorting, discrete-event processing or index reduction. In this paper, an open source tool according to OOM paradigm and developed in MATLAB is introduced. It implements such algorithms adding an educational perspective about how they work, since the step by step results obtained after processing the model equations can be shown. The tool also allows to create models using its own OOM language and to simulate the final executable equation set. It was used by students in a modelling and simulation course of the Automatic Control and Industrial Electronics Engineering degree, showing a significant improvement in their understanding and learning of the abovementioned topics after their assessment

Recomputing Causality Assignments on Lumped Process Models When Adding New Simplification Assumptions

This paper presents a new algorithm for the resolution of over-constrained lumped process systems, where partial differential equations of a continuous time and space model of the system are reduced into ordinary differential equations with a finite number of parameters and where the model equations outnumber the unknown model variables. Our proposal is aimed at the study and improvement of the algorithm proposed by Hangos-Szerkenyi-Tuza. This new algorithm improves the computational cost and solves some of the internal problems of the aforementioned algorithm in its original formulation. The proposed algorithm is based on parameter relaxation that can be modified easily. It retains the necessary information of the lumped process system to reduce the time cost after introducing changes during the system formulation. It also allows adjustment of the system formulations that change its differential index between simulations

Constructing the world: Active causal learning in cognition

Humans are adept at constructing causal models of the world that can support prediction, explanation, simulation-based reasoning, planning and control. In this thesis I explore how people learn about the causal world interacting with it, and how they represent and modify their causal knowledge as they gather evidence. Over 10 experiments and modelling, I show that interventional and temporal cues, along with top-down hierarchical constraints, inform the gradual evolution and adaptation of increasingly rich causal representations. Chapters 1 and 2 develop a rational analysis of the problems of learning and representing causal structure, and choosing interventions, that perturb the world in ways that reveal its structure. Chapters 3--5 focus on structure learning over sequences of discrete trials, in which learners can intervene by setting variables within a causal system and observe the consequences. The second half of the thesis generalises beyond the discrete trial learning case, exploring interventional causal learning in situations where events occur in continuous time (Chapters 6 and 7); and in spatiotemporally rich physical "microworlds" (Chapter 8). Throughout the experiments, I find that both children and adults are robust active causal learners, able to deal with noise and complexity even as normative judgment and intervention selection become radically intractable. To explain their success, I develop scalable process level accounts of both causal structure learning and intervention selection inspired by approximation algorithms in machine learning. I show that my models can better explain patterns of behaviour than a range of alternatives as well as shedding light on the source of common biases including confirmatory testing, anchoring effects and probability matching. Finally, I propose a close relationship between active learning and active aspects of cognition including thinking, decision making and executive control