A quantitative probabilistic investigation into the accumulation of rounding errors in numerical ODE solution.

We examine numerical rounding errors of some deterministic solvers for systems of ordinary differential equations (ODEs) from a probabilistic viewpoint. We show that the accumulation of rounding errors results in a solution which is inherently random and we obtain the theoretical distribution of the trajectory as a function of time, the step size and the numerical precision of the computer. We consider, in particular, systems which amplify the effect of the rounding errors so that over long time periods the solutions exhibit divergent behaviour. By performing multiple repetitions with different values of the time step size, we observe numerically the random distributions predicted theoretically. We mainly focus on the explicit Euler and fourth order Runge–Kutta methods but also briefly consider more complex algorithms such as the implicit solvers VODE and RADAU5 in order to demonstrate that the observed effects are not specific to a particular method

Stochastic rounding and reduced-precision fixed-point arithmetic for solving neural ordinary differential equations

Although double-precision floating-point arithmetic currently dominates high-performance computing, there is increasing interest in smaller and simpler arithmetic types. The main reasons are potential improvements in energy efficiency and memory footprint and bandwidth. However, simply switching to lower-precision types typically results in increased numerical errors. We investigate approaches to improving the accuracy of reduced-precision fixed-point arithmetic types, using examples in an important domain for numerical computation in neuroscience: the solution of Ordinary Differential Equations (ODEs). The Izhikevich neuron model is used to demonstrate that rounding has an important role in producing accurate spike timings from explicit ODE solution algorithms. In particular, fixed-point arithmetic with stochastic rounding consistently results in smaller errors compared to single precision floating-point and fixed-point arithmetic with round-to-nearest across a range of neuron behaviours and ODE solvers. A computationally much cheaper alternative is also investigated, inspired by the concept of dither that is a widely understood mechanism for providing resolution below the least significant bit (LSB) in digital signal processing. These results will have implications for the solution of ODEs in other subject areas, and should also be directly relevant to the huge range of practical problems that are represented by Partial Differential Equations (PDEs).Comment: Submitted to Philosophical Transactions of the Royal Society

Mathematical modeling of SGK1 dynamics in medulloblastoma tumor cells

This work is devoted to mathematical modeling of deregulation of the Wnt/β-catenin signaling pathway in medulloblastoma resulting in abnormal dynamics of target genes. Medulloblastoma is a brain tumor, mostly diagnosed in children. It is associated with several molecular genetic alterations. Specific aberrations of chromosome 6q, leading either to the chromosome copy-number loss (monosomy 6) or gain (trisomy 6), occur in two different subtypes of the tumor. The model is a nine-dimensional system of ordinary differential equations and describes nonlinear dynamics of the key ingredients of the signaling process. The model is based on the law of mass action and accounts for a two-compartment architecture of a cell consisting of the nucleus and cytoplasm. The model helps to understand molecular differences between the two medulloblastoma mutation subtypes that are associated with different patient prognosis. Our studies are based on a collaboration with the group of Prof. Dr. med. Stefan Pfister at the Division of Pediatric Neuro-oncology Research Group of the German Cancer Research Center (DKFZ). The model is used to evaluate data from the gene expression microarray data from the clinics in Heidelberg, Boston and Amsterdam. Numerical simulations lead to new biological hypotheses related to a significant role of the regulatory loop SGK1-GSK3β-MYC, a part of the Wnt/β-catenin signaling pathway. Simulations indicate the advantage of using the pharmacological inhibitor of SGK1 in patients with copy-number gain of chromosome 6q. Finally, the simulation results suggest a beneficial use of an adjuvant therapy in a trisomy 6 treatment. Mathematical analysis of the ordinary differential equations system confirms the wellposedness of the model and provides basic properties of the solutions. Supported by numerical analysis, we conclude about global stability of a unique positive equilibrium corresponding to the homeostasis of the system. We also tackle the parameter estimation problem using statistical assessment of the results and Gauss-Newton method. Sensitivity analysis provides insight into the role of model parameters. In particular, it confirms the sensitivity of the system to the parameter of SGK1 degradation. The model provides a powerful tool to study mechanistically the underlying process and to support the experiments

Persistent mutual information

We study Persistent Mutual Information (PMI), the information about the past that persists into the future as a function of the length of an intervening time interval. Particularly relevant is the limit of an infinite intervening interval, which we call Permanently Persistent MI. In the logistic and tent maps PPMI is found to be the logarithm of the global periodicity for both the cases of periodic attractor and multi-band chaos. This leads us to suggest that PPMI can be a good candidate for a measure of strong emergence, by which we mean behaviour that can be forecast only by examining a specific realisation. We develop the phenomenology to interpret PMI in systems where it increases indefinitely with resolution. Among those are area-preserving maps. The scaling factor r for how PMI grows with resolution can be written in terms of the combination of information dimensions of the underlying spaces. We identify r with the extent of causality recoverable at a certain resolution, and compute it numerically for the standard map, where it is found to reflect a variety of map features, such as the number of degrees of freedom, the scaling related to existence of different types of trajectories, or even the apparent peak which we conjecture to be a direct consequence of the stickiness phenomenon. We show that in general only a certain degree of mixing between regular and chaotic orbits can result in the observed values of r. Using the same techniques we also develop a method to compute PMI through local sampling of the joint distribution of past and future. Preliminary results indicate that PMI of the Double Pendulum shows some similar features, and that in area-preserving dynamical systems there might be regimes where the joint distribution is multifractal

Mathematical Methods, Modelling and Applications

This volume deals with novel high-quality research results of a wide class of mathematical models with applications in engineering, nature, and social sciences. Analytical and numeric, deterministic and uncertain dimensions are treated. Complex and multidisciplinary models are treated, including novel techniques of obtaining observation data and pattern recognition. Among the examples of treated problems, we encounter problems in engineering, social sciences, physics, biology, and health sciences. The novelty arises with respect to the mathematical treatment of the problem. Mathematical models are built, some of them under a deterministic approach, and other ones taking into account the uncertainty of the data, deriving random models. Several resulting mathematical representations of the models are shown as equations and systems of equations of different types: difference equations, ordinary differential equations, partial differential equations, integral equations, and algebraic equations. Across the chapters of the book, a wide class of approaches can be found to solve the displayed mathematical models, from analytical to numeric techniques, such as finite difference schemes, finite volume methods, iteration schemes, and numerical integration methods

Simulation Modeling

The book presents some recent specialized works of a theoretical and practical nature in the field of simulation modeling, which is being addressed to a large number of specialists, mathematicians, doctors, engineers, economists, professors, and students. The book comprises 11 chapters that promote modern mathematical algorithms and simulation modeling techniques, in practical applications, in the following thematic areas: mathematics, biomedicine, systems of systems, materials science and engineering, energy systems, and economics. This project presents scientific papers and applications that emphasize the capabilities of simulation modeling methods, helping readers to understand the phenomena that take place in the real world, the conditions of their development, and their effects, at a high scientific and technical level. The authors have published work examples and case studies that resulted from their researches in the field. The readers get new solutions and answers to questions related to the emerging applications of simulation modeling and their advantages