Mauricio Secchi

Mauricio Secchi

University of Chieti-Pescara
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Mauricio Secchi has a total of 4 co-authors. Here are the top co-authors by number of shared publications:

Latest Publications

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    PyDebrisFlow2D: A 2D Conservative Python Multiphysics Solver for Variable-Density Debris Flows with Voellmy Resistance, Bed Exchange, and Grain Size Segregation

    by Mauricio Secchi, Antonio Pasculli, Massimo Mangifesta, Nicola Sciarra

    Reliable debris-flow modelling requires the coupled treatment of rapid free-surface propagation, variable mixture composition, wet–dry fronts, basal resistance, bed exchange, and grain size segregation. This work presents PyDebrisFlow2D, a depth-averaged Python (3.13.14) solver based on seven conservative variables describing mobile fluid, layered fine and coarse solids, and two horizontal mixture momentum components. Mixture density and composition-dependent Voellmy resistance are derived consistently from the transported constituent volumes, while conservative source operators account for erosion, entrainment, class-resolved deposition, and segregation opposed by diffusive remixing. The governing equations are solved using a Cartesian finite volume method with hydrostatic reconstruction, Harten–Lax–van Leer–Contact (HLLC) fluxes, optional Monotonic Upstream-Centered Scheme for Conservation Laws (MUSCL) reconstruction, and first- or second-order time integration. Local Harten–Lax–van Leer (HLL) fallback, stage repetition, positivity repair, and adaptive time-step rejection provide additional robustness near wet–dry interfaces and strong gradients. Controlled verification experiments confirmed flux consistency, well-balanced behaviour, constituent conservation, and stable convergence for different slope limiters. The verification programme also examines the reduced segregation formulation, an alternative concentration-aware resistance closure, and the applicability limits of the depth-averaged model through targeted real-topography analyses. Across the Ritter refinement experiment, the nominal second-order scheme required 1.91–3.13× the first-order (O1) wall time while reducing the depth error; at the finest grid, the relative L1 error was 13.35× smaller for a 2.309× runtime increase. A propagation-only application on the Marsicano digital elevation model reproduced the principal channelized path and terminal accumulation area while conserving the released volume. A complementary ablation analysis showed that erosion, deposition, and segregation produce physically coherent and conservative modifications of the simulated flow without introducing numerical mass imbalance, while CUDA speedup over a 24-thread CPU reached 67.39× for the largest Marsicano benchmark. These results provide process-level verification of the numerical implementation and its multiphysics coupling, although they do not constitute observational validation of the simulated event. The Marsicano comparison offers a qualitative cross-model consistency check against a published scenario produced with the established Rapid Mass Movement Simulation (RAMMS) framework, which has been extensively applied and evaluated in real-event studies. The solver, verification tests, and reproducible configurations are publicly available as free and open-source research software. Event-scale predictive applications nevertheless require site-specific calibration and independent validation against field observations.

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    Full-Flowfield Inversion of Debris-Flow Dynamics: A Global–Local Optimization Framework for Model Parameter Estimation from Final Accumulation Fields

    by Mauricio Secchi, Massimo Mangifesta, Nicola Sciarra

    Calibration of debris-flow models is important for reproducible hazard mapping, transparent scenario analysis, and risk-informed territorial planning. Reliable calibration of debris-flow models is limited by parameter uncertainty, non-uniqueness, and the loss of spatial information when observations are reduced to scalar targets. This study presents a global–local full-flowfield inversion framework for estimating effective Voellmy friction parameters from complete final-thickness rasters, thereby replacing subjective trial-and-error adjustment with an explicit forward–inverse workflow and a spatially distributed objective function. A two-dimensional finite-volume shallow-flow solver is coupled with a genetic algorithm for bounded global exploration and projected finite-difference gradient descent for local refinement. The composite objective combines thickness, wet footprint, signed boundary distance, and total volume mismatches. The framework was evaluated through controlled parameter recovery, objective profiling, ten-seed repeatability tests, observation perturbations, conditional identifiability analysis, and a controlled Morino–Rendinara back-analysis retrieval test based on a published real-event parameterization and the real terrain. Uniform reference coefficients of 0.10, 0.25, and 0.45 were recovered with a mean absolute error of 1.90×10−4 and exact wet footprint agreement. Across ten baseline inversions, the standard deviation of the estimate was 1.53×10−4. Random removal of up to 50% of observation cells had negligible influence, whereas thickness noise and systematic scaling produced larger deviations; at 20% noise, mean IoU remained 0.9861. Prescribing ξ between 250 and 1000 m s−2 shifted the estimated μ from 0.243038 to 0.254628 despite nearly identical final deposits, demonstrating conditional identifiability. In the two-zone experiment, the framework recovered μup=0.009991 and μdown=0.100184, with a held-out RMSE of 4.49×10−5 m and IoU equal to one. Full-flowfield inversion therefore provides accurate and reproducible event-specific parameter retrieval.

    Cited by 1
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    Artificial Intelligence for Learning 2D Debris-Flow Dynamics: Application of Fourier Neural Operators and Synthetic Data to a Case Study in Central Italy

    by Mauricio Secchi, Antonio Pasculli, Nicola Sciarra

    Physics-based simulation of debris flows over complex terrain is essential for hazard assessment, but repeated numerical integration is costly when many scenarios must be explored. We develop a general deep-learning surrogate modelling framework for two-dimensional (2D) debris-flow propagation, here applied to the Morino–Rendinara area (central Italy) using a three-dimensional (3D) Fourier Neural Operator (FNO) trained on synthetic simulations generated by a validated in-house finite-volume shallow-water solver. The solver reproduces debris-flow propagation over complex terrain and is specifically developed for artificial intelligence (AI) applications. It is based on a depth-averaged 2D formulation using the Harten–Lax–van Leer–Contact (HLLC) approximate Riemann solver, hydrostatic reconstruction, positivity-preserving wet–dry treatment, and Voellmy-type basal friction, and was verified through analytical benchmarks, numerical tests, and back-analyses of real events. The dataset was built from four site-specific release settings derived from real topography, combining different released volumes and bulk densities while preserving local geomorphological and rheological characteristics. Each simulation was stored as a full spatio-temporal tensor and used to train an FNO conditioned on coordinates, topography, friction parameters, bulk density, and initial release thickness. Training used a novel loss to emphasize active-flow areas and improve velocity reconstruction, and was performed using a graphics processing unit (GPU). The surrogate shows effective generalization to within-distribution validation samples, with global relative mean squared errors of 5.49% for flow thickness, 5.34% for velocity component u, and 2.60% for v, and mean R2 values of 0.95, 0.94, and 0.97. For a representative sample, the surrogate predicts the full spatio-temporal solution in 0.52 s, versus about 47 s for the first-order finite-volume solver, corresponding to a speed-up of about 91×, with an even larger gap expected for higher-order solvers, since, whilst the computation time of the solver increases as its complexity increases, the computation time of the FNO remains essentially unchanged. These results indicate that the proposed FNO is a reliable site-specific surrogate for rapid approximation of 2D debris-flow dynamics over real terrain, with potential for uncertainty propagation, Monte Carlo analysis, large-ensemble simulation, and hazard-oriented scenario assessment.

    Cited by 2
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    Learning Debris Flow Dynamics with a Deep Learning Fourier Neural Operator: Application to the Rendinara–Morino Area

    by Mauricio Secchi, Antonio Pasculli, Massimo Mangifesta, Nicola Sciarra

    Accurate numerical simulation of debris flows is essential for hazard assessment and early-warning design, yet high-fidelity solvers remain computationally expensive, especially when large ensembles must be explored under epistemic uncertainty in rheology, initial conditions, and topography. At the same time, field observations are typically sparse and heterogeneous, limiting purely data-driven approaches. In this work, we develop a deep-learning Fourier Neural Operator (FNO) as a fast, physics-consistent surrogate for one-dimensional shallow-water debris-flow simulations and demonstrate its application to the Rendinara–Morino system in central Italy. A validated finite-volume solver, equipped with HLLC and Rusanov fluxes, hydrostatic reconstruction, Voellmy-type basal friction, and robust wet–dry treatment, is used to generate a large ensemble of synthetic simulations over longitudinal profiles representative of the study area. The parameter space of bulk density, initial flow thickness, and Voellmy friction coefficients is systematically sampled, and the resulting space–time fields of flow depth and velocity form the training dataset. A two-dimensional FNO in the (x,t) domain is trained to learn the full solution operator, mapping topography, rheological parameters, and initial conditions directly to h(x,t) and u(x,t), thereby acting as a site-specific digital twin of the numerical solver. On a held-out validation set, the surrogate achieves mean relative L2 errors of about 6–7% for flow depth and 10–15% for velocity, and it generalizes to an unseen longitudinal profile with comparable accuracy. We further show that targeted reweighting of the training objective significantly improves the prediction of the velocity field without degrading depth accuracy, reducing the velocity error on the unseen profile by more than a factor of two. Finally, the FNO provides speed-ups of approximately 36× with respect to the reference solver at inference time. These results demonstrate that combining physics-based synthetic data with operator-learning architectures enables the construction of accurate, computationally efficient, and site-adapted surrogates for debris-flow hazard analysis in data-scarce environments.

    Cited by 7
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    Learning Debris Flow Dynamics with a Deep Learning Fourier Neural Operator: Application to the Rendinara–Morino Area

    by Mauricio Secchi, Antonio Pasculli, Massimo Mangifesta, Nicola Sciarra

    Accurate numerical simulation of debris flows is essential for hazard assessment and early-warning design, yet high-fidelity solvers remain computationally expensive, especially when large ensembles of scenarios must be explored under epistemic uncertainty in rheology, initial conditions, and topography. At the same time, field observations are typically sparse and heterogeneous, limiting the direct use of purely data-driven approaches. In this work, we develop a Deep-Learning Fourier Neural Operator (FNO) as a fast and accurate surrogate for one-dimensional shallow-water debris-flow simulations, and we demonstrate its application to characterizing the Rendinara–Morino debris-flow system in central Italy. A validated finite-volume solver with HLLC and Rusanov fluxes, Voellmy-type basal friction, hydrostatic reconstruction, and robust wet–dry treatment is used to generate a large ensemble of synthetic simulations over longitudinal profiles representative of the study area. The parameter space of bulk density, initial flow thickness, and Voellmy friction coefficients is systematically sampled, and the resulting space–time fields of flow depth and velocity form the training dataset. A two-dimensional FNO in the (x,t) plane is trained to map coordinates, rheological parameters, bed elevation, and initial conditions to the full evolution of depth and velocity. On a held-out validation set, the surrogate attains mean relative L2 errors below about 6% for flow depth and 10–15% for velocity, including prediction on an unseen topographic profile, while providing speed-ups of up to 36× (several orders of magnitude) compared to the numerical solver. These results show that combining physics-based synthetic data with operator-learning architectures enables the construction of site-specific, computationally efficient surrogates for debris-flow hazard analysis in data-scarce environments.

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    A Novel Probabilistic Approach for Debris Flow Accumulation Volume Prediction Using Bayesian Neural Networks with Synthetic and Real-World Data

    by Antonio Pasculli, Mauricio Secchi, Massimo Mangifesta, Corrado Cencetti, Nicola Sciarra

    Debris flow events are complex natural phenomena that are challenging to predict, especially when data are limited or uncertain. This study presents a novel probabilistic approach using Bayesian Neural Networks (BNN) to predict possible volumes of debris flow accumulation by using synthetic and real-world data. Synthetic datasets are created based on statistical distributions informed by geomorphological and hydrological knowledge, allowing the model to learn typical behaviors when real data is scarce. BNN provide uncertainty quantification by modeling neural weights as probability distributions. The model resulting from validation on synthetic data and two real datasets from China and South Korea show strong predictive performance (R2 > 0.98) and close alignment between predicted and observed volumes, even in the presence of outliers. The key strength of this integrated approach lies in its integration of synthetic data generation, real data augmentation based on Bootstrapping, expert knowledge and Bayesian deep learning to overcome limitations of traditional statistical models, improving debris flow forecasting and enabling more informed and resilient risk management strategies.

    Cited by 6