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Fasma Diele

Publications and source records attributed to Fasma Diele.

6 recordsLinked to original sources

Turing-region preservation in matrix-oriented splitting methods for reaction-diffusion systems

We develop matrix-oriented formulations of first-order splitting integrators for two-species reaction-diffusion systems on 2D domains, using the Gierer-Meinhardt system as a benchmark for discrete Turing instability. Exploiting the differential matrix equation associated with tensor-product spatial discretizations, we obtain the families IE-S and EX-S, where the diffusive flow is treated by implicit Euler or exactly, respectively, and the reaction substep is approximated by explicit, symplectic, adjoint-symplectic, Poisson, and explicit-variant local maps. Starting from the continuous diffusion-driven instability threshold, expressed through the modal relation $J_\mu = J^* - \mu D$, we derive fully discrete modal amplification matrices and their Jury conditions. These conditions separate the continuous Turing mechanism, carried by the first Jury condition, from discrete effects carried by the second. Specializing the analysis to the Gierer-Meinhardt model, we exhibit two opposite pathologies. First, in a continuous Turing-stable regime, IMEX may generate a stable spurious pattern through a violation of the second Jury condition. Second, in a real continuous Turing regime, the adjoint-symplectic family may be spuriously stable and suppress the pattern that IMEX correctly detects. For IMEX, whose first Jury condition reproduces exactly the sign of the continuous Turing polynomial, we give an explicit time-step condition guaranteeing preservation of the continuous Turing region, and show that controlling the second Jury condition is needed only in the Turing-stable regime. These examples show that each integrator induces its own discrete Turing region, which should be compared with the continuous one before interpreting numerical patterns; we frame this requirement as preserving a qualitative property of the continuous problem, in the spirit of structure-preserving numerical integration.

math.NA

Exponential Consensus through Z-Control in High-Order Multi-Agent Systems

In this work, we introduce a Z-control strategy for multi-agent systems of arbitrary order, aimed at driving the agents toward consensus in the highest-order observable state. The proposed framework supports both direct and indirect control schemes, making it applicable in scenarios where high-order derivatives such as acceleration cannot be directly manipulated. Theoretical analysis ensures exponential convergence while preserving the average dynamics, and a hierarchy of control laws is derived accordingly. Numerical experiments up to third-order models, including opinion dynamics and Cucker-Smale flocking systems, demonstrate the robustness and flexibility of Z-control under varying interaction regimes and control intensities.

math.OC

Hierarchical clustering and dimensional reduction for optimal control of large-scale agent-based models

Agent-based models (ABMs) provide a powerful framework to describe complex systems composed of interacting entities, capable of producing emergent collective behaviours such as consensus formation or clustering. However, the increasing dimensionality of these models -- in terms of both the number of agents and the size of their state space -- poses significant computational challenges, particularly in the context of optimal control. In this work, we propose a scalable control frame work for large-scale ABMs based on a twofold model order reduction strategy: agent clustering and projection-based reduction via Proper Orthogonal Decomposition (POD). These techniques are integrated into a feedback loop that enables the design and application of optimal control laws over a reduced-order representation of the system. To illustrate the effectiveness of the approach, we consider the opinion dynamics model, a prototyp ical first-order ABM where agents interact through state-dependent influence functions. We show that our method significantly improves control efficiency, even in scenarios where direct control fails due to model complexity. Beyond its methodological contributions, this work also highlights the rel evance of opinion dynamics models in environmental contexts -- for example, modeling the diffusion of pro-environmental attitudes or decision-making processes in sustainable policy adoption -- where controlling consensus formation plays a crucial role.

math.OC

Transient Instability and Patterns of Reactivity in Diffusive-Chemotaxis Soil Carbon Dynamics

We study pattern formation in a chemotaxis model of bacteria and soil carbon dynamics as an example system where transient dynamics can give rise to pattern formation outside of Turing unstable regimes. We use a detailed analysis of the reactivity of the non-spatial and spatial dynamics, stability analyses, and numerical continuation to uncover detailed aspects of this system's pattern-forming potential. In addition to patterning in Turing unstable parameter regimes, reactivity of the spatial system can itself lead to a range of parameters where a spatially uniform state is asymptotically stable, but exhibits transient growth that can induce pattern formation. We show that this occurs in the bistable region of a subcritical Turing bifurcation. Intriguingly, such bistable regions appear in two spatial dimensions, but not in a one-dimensional domain, suggesting important interplays between geometry, transient growth, and the emergence of multistable patterns. We discuss the implications of our analysis for the bacterial soil organic carbon system, as well as for reaction-transport modeling more generally.

math.NA

Patterns in soil organic carbon dynamics: integrating microbial activity, chemotaxis and data-driven approaches

Models of soil organic carbon (SOC) frequently overlook the effects of spatial dimensions and microbiological activities. In this paper, we focus on two reaction-diffusion chemotaxis models for SOC dynamics, both supporting chemotaxis-driven instability and exhibiting a variety of spatial patterns as stripes, spots and hexagons when the microbial chemotactic sensitivity is above a critical threshold. We use symplectic techniques to numerically approximate chemotaxis-driven spatial patterns and explore the effectiveness of the piecewice dynamic mode decomposition (pDMD) to reconstruct them. Our findings show that pDMD is effective at precisely recreating chemotaxis-driven spatial patterns, therefore broadening the range of application of the method to classes of solutions different than Turing patterns. By validating its efficacy across a wider range of models, this research lays the groundwork for applying pDMD to experimental spatiotemporal data, advancing predictions crucial for soil microbial ecology and agricultural sustainability.

math.NA

Evaluating the impact of increasing temperatures on changes in Soil Organic Carbon stocks: sensitivity analysis and non-standard discrete approximation

A novel model is here introduced for the SOC change index defined as the normalized difference between the actual Soil Organic Carbon and the value assumed at an initial reference year. It is tailored on the RothC carbon model dynamics and assumes as baseline the value of the SOC equilibrium under constant environmental conditions. A sensitivity analysis is performed to evaluate the response of the model to changes of temperature, Net Primary Production (NPP), and land use soil class (forest, grassland, arable). A non-standard monthly time-stepping procedure has been proposed to approximate the SOC change index in the Alta Murgia National Park, a protected area in the Italian Apulia region, selected as test site. In the case of arable class, the SOC change index exhibits a negative trend which can be inverted by a suitable organic fertilization program here proposed.

math.NA