Papers
Machine learning for physics, as it is published.
New work from arXiv on CFD surrogates, neural operators and physics-informed learning, refreshed every hour. Search it, filter by topic, or start with the papers we keep coming back to.
Papers
New work from arXiv on CFD surrogates, neural operators and physics-informed learning, refreshed every hour. Search it, filter by topic, or start with the papers we keep coming back to.
1,058 papers in CFD surrogates · newest first
cs.AI
Predictive models for laser powder bed fusion (LPBF) are rarely used on the shop floor because querying them requires programming and model-specific knowledge. We present AgentAM, a large language model (LLM) agent that gives process engineers natural-language access to CFD-trained surrogates of spatter and melt pool geometry for SS316L through the Model Context Protocol (MCP). Polynomial surrogates trained on 279 single-track simulations predict melt pool dimensions () and spatter volume ( on test data) at any power-velocity point in the trained range, and geometric criteria assign each point a geometry-derived defect regime (keyhole, lack of fusion, balling or stable) that matches the regime obtained from the simulated geometry in 93.4% of tracks under cross-validation. The agent selects and parameterizes tools for prediction, process-window scans and constrained optimization. Minimum-spatter optima often sat within 10 m of the lack-of-fusion boundary; a margin-aware optimizer moved them away from all thresholds and raised the estimated probability of remaining classified Good from 0.94 to 1.00 under an assumed 2% process drift, and from 0.72 to 0.91 when jointly sampled cross-validated geometry residuals were added. About 40-50 well-spread simulations sufficed to reproduce the defect map. To evaluate the agent layer, we introduce a 145-prompt benchmark scored against direct tool calls, plus eight prompts (40 runs) on margin-aware requests. Over the 725 runs of the main benchmark the agent passed the intended inputs in 98.5% of parsing prompts, reported the scored spatter values and defect classes faithfully and met every stated constraint in the 65 feasible optimization runs, within a median 1.0% of the brute-force optimum.
cs.LG
This paper presents a CFD dataset of a highly parametrized 3D wing operating in ground effect, designed for benchmarking neural surrogate models for aerodynamic prediction, using a wing shape and flow conditions typically encountered in motorsport applications. The dataset comprises 770 RANS simulations of 194 unique wing configurations, sampled across 27 airfoil profile variants and 4 distances from the ground, spanning variations in wing sweep, chord, taper, angle of attack, and tip twist. Six neural network architectures representing distinct paradigms — convolutional (DoMINO), graph-based (X-MeshGraphNet), transformer-based (AB-UPT, GeoTransolver, GAOT), and JEPA-based (AeroJEPA) — are evaluated on their ability to predict surface pressure coefficient and wall shear stress distributions, from which integral drag and lift coefficients are recovered. The dataset is intended to be used as a challenging benchmark for surrogate CFD models, due to the uniqueness of the ground effect being absent from other wing and airfoil datasets.
cs.CV
Intracardiac flow is driven by complex, and time-varying cardiac motion and interaction with the heart valves, producing complex flow features that may provide valuable information for disease diagnosis and treatment planning. 4D Flow MRI enables non-invasive, time-resolved measurement of three-dimensional cardiovascular velocity fields. However, substantial noise and limited spatial and temporal resolution limit its ability to accurately resolve flow features. We propose 4D-MRAR (4D Magnetic Resonance Augmented Reconstruction), a deep learning (DL) method to super-resolve and denoise 4D Flow MRI data. Unlike prior DL methods that train and infer on static vascular regions, our method predicts intracardiac flow within hearts undergoing large deformation. Moreover, it uniquely allows for simultaneous super-resolution in 3D space and time. The method combines a residual convolutional neural network, a recurrent neural network, and an implicit neural field to predict high-resolution velocity and pressure fields while efficiently enforcing the governing equations of fluid flow during training. Trained on a synthetic dataset obtained from computational fluid dynamics (CFD) simulations of left ventricle (LV) flow, our method generalizes to synthetic whole-heart 4D flow MRI data, and demonstrated promising performance on real 4D Flow MRI. The produced super-resolved flow fields exhibit significantly improved accuracy and greater consistency with the governing equations of blood flow compared with prior methods.
cs.LG
Neural operators provide efficient surrogates for computational fluid dynamics (CFD), but most are designed for static geometries and are not equipped to model unsteady flows induced by continuously moving boundaries. We study dynamic-boundary 4D CFD prediction for articulated moving geometries and introduce RoboCFD-4D, a temporally resolved robotic CFD benchmark spanning diverse morphologies and flow conditions. To address the computational and autoregressive challenges of this setting, we propose DynaSolver, a neural operator that explicitly conditions on boundary evolution and causal flow history. DynaSolver employs an efficient physical-token formulation that amortizes point-to-token projection across the operator stack, substantially reducing repeated point-space spatio-temporal computation while retaining accurate point-level reconstruction. We further introduce Replay-Augmented Parallel Training, which exposes the model to autoregressively generated histories while preserving parallel dense supervision, narrowing the gap between ground-truth-conditioned training and autoregressive rollout. Experiments on RoboCFD-4D show that DynaSolver achieves the best full-rollout accuracy among a broad range of learned CFD surrogates, with its advantage increasing as rollout proceeds, indicating substantially reduced autoregressive error accumulation. Project page: https://made-gpt.github.io/DynaSolver-ProjectPage.
physics.flu-dyn
Laminar-turbulent transition governs skin friction, heat transfer and losses in turbomachinery, yet Reynolds-averaged Navier-Stokes (RANS) transition models rely on empirical correlations and hand-calibrated activation functions whose structural deficiencies cannot be resolved by recalibration. We propose a CFD-driven sparse symbolic regression framework to learn interpretable, closed-form corrections to the production and destruction activation functions in the intermittency equation of the local, Galilean-invariant -- model of Ge et al. Each correction combines a polynomial of a local pressure-gradient parameter, controlling amplitude and sign, with a bounded exponential of local flow features, controlling spatial support. This construction bounds the corrections without ad hoc limiters. Coefficients are identified by gradient-free optimization with the RANS solver in the loop, accelerated by POD-Kriging field surrogates, using mean-velocity data from wall-resolved large-eddy simulations (LES) of the ERCOFTAC T3 flat-plate cases. Elastic-net hyperparameters are selected automatically through bi-level optimization with leave-one-case-out cross-validation. The resulting model retains 11 of 21 candidate terms, compared with 20 under conventional grid-search tuning, while achieving comparable or slightly better accuracy on training and unseen cases. Both approaches recover the same leading-order correction. Applied without retraining to the transonic LS89 turbine cascade, far beyond the training envelope, the augmented model remains numerically robust and improves heat-transfer predictions at all four operating points, reducing errors by up to 70% where the baseline predicts premature transition under high freestream turbulence. Residual errors in transition onset and length highlight the limits of training on flat-plate data alone.
cs.CE
Neural surrogates can accelerate many-query aerodynamic analyses, but their standalone predictions may be insufficiently accurate or robust to support reliable design evaluations. We develop a learned warm-start formulation that predicts mean-flow and turbulence fields directly on native CFD meshes for subsequent correction by the RANS solver. A common architecture and objective structure are used across subcritical and transonic flows, including challenging conditions with shock-induced separation. A condition-modulated Transolver combines Fourier and wall features with target-gated regularization, a Sobolev-type loss, adaptive loss weighting and sampled training. Prediction requires no preliminary flow solution, and training requires no solver-residual evaluation. Extensive 3D CFD evaluations on SuperWing, the NASA Common Research Model (CRM) and DLR-F6 assess geometric and operating-condition generalization, including interpolation, extrapolation and cross-mesh deployment, using Spalart-Allmaras and - SST closures on million-cell meshes. Unseen nominal cases achieve geometric-mean iteration speed-ups of approximately - under a common convergence criterion based on cold-run aerodynamic coefficients. On CRM, learned initialization provides greater aggregate acceleration than nearest-condition restarts using the same solution library. Resolution invariance enables direct deployment of coarse-mesh models on finer meshes without retraining, solution interpolation or target-condition coarse solves. Including data generation and training, estimated campaign speed-ups reach -. No warm run diverges; fewer than fail to satisfy the cold-reference convergence criterion. These results support learned initialization for repeated aerodynamic analyses, while highlighting initialization sensitivity near separation as a remaining limitation.
math-ph
High-fidelity CFD is essential for coupled hypersonic aerodynamic and aerothermal phenomena, but its cost precludes multi-objective design optimization. We develop three DeepONet surrogates for two hypersonic configurations, unified by one device: the trunk receives coordinates in which the varying feature is held fixed. The first is a prescribed family of 37 spherically blunted tangent ogives, varied through ogive radius of curvature to span nose-tip ablation forebody change; no ablation is computed. A body-fitted canonical transformation maps every domain onto a common (s, eta) space, retaining physical coordinates for forces and heat transfer. A branch network predicts reduced coefficients and a Transolver trunk with Physics-Attention learns the spatial basis; a two-step strategy optimizes both jointly, orthonormalizes the basis by QR factorization, and trains a second-stage branch for unseen geometries. Surrogates cover density, pressure, velocity, temperatures, conductivities and species mass fractions. In leave-one-out tests, drag, mean and peak wall heat flux are recovered to 1.16%, 1.62% and 5.29%; a sweep of 1001 geometries gives a 200-design Pareto front trading 6.68% in drag for 4.42% in peak heating (a diagnostic, not an objective). The second is the 3D AEDC waverider at 29 angles of attack: static mesh, moving leading-edge shock. For surface quantities, the same construction predicts integrated heat load within 0.22% and force coefficients to errors below 2.6x10^-5; Pareto fronts identify angles of attack trading drag, lift and heating. For the volume field, a Cartesian trunk smears the shock; a frozen ShockNet supplies a shock-aligned coordinate and a hotspot-aware mixture of experts corrects the residual, recovering density to 12.28% relative L2 error at a held-out incidence (5.08x10^7 nodes). The 1001-design sweep takes about three minutes on a single CPU.
cs.LG
Recent advances in machine learning (ML) have the potential to substantially accelerate aircraft wing design, a process that has traditionally required extensive high-fidelity simulation and design iteration. Existing public datasets for ML-based wing design have focused primarily on aerodynamic performance, with limited consideration of the multidisciplinary interactions that govern practical aircraft design. In particular, efficient wing design requires simultaneous consideration of aerodynamic performance and structural integrity, yet no publicly available datasets capture this coupled aerostructural behavior at high-fidelity. To address this need, a high-fidelity multidisciplinary dataset for static aerostructural analysis of transonic wings is introduced. The dataset includes 2471 samples, along with three out-of-distribution samples. The wing planform geometry is parameterized by span, sweep, dihedral, tip chord, and tip twist, while the airfoil cross-sections are parameterized using the class-shape transformation method. The analysis is further parameterized by Mach number, angle of attack, altitude, and structural load factor. The utility of the dataset is demonstrated by training state-of-the-art ML models to predict aerodynamic surface quantities, integrated aerodynamic performance quantities, and the failure distribution of the wingbox. ML models achieve at most 0.0309 normalized root mean squared error (NRMSE) in the prediction of aerodynamic quantities and at most 0.1189 NRMSE in the prediction of failure distribution of the wingbox. By providing coupled aerodynamic and structural responses for a diverse set of transonic wings, this dataset establishes a benchmark for developing, evaluating, and comparing multi-physics ML models for wing design. Broadly, it is intended to encourage creation of similar high-fidelity multi-physics benchmark datasets across engineering domains.
math.OC
The curse of dimensionality associated with solving the Hamilton-Jacobi-Bellman equation for the value function remains a key obstacle for real-time optimal feedback control of high-dimensional nonlinear dynamical systems. This is especially severe in aerospace applications such as relaminarizing unsteady turbulent flows and recovering from aerodynamic stall where nonlinear dynamics play a significant role. We introduce Balanced Active Subspaces for Optimal Control (BASOC), a model reduction framework which seeks to find directions in state space along which the value function is most sensitive, and the distribution of states exhibits maximum variance along optimal trajectories. BASOC leverages trajectory optimization and the Pontryagin minimum principle to obtain state and co-state (value function gradient) data along locally optimal trajectories for constructing state and gradient covariance matrices. Balancing these matrices provides an oblique projection for approximating the value function, which can also be used to construct reduced-order models and surrogate feedback control laws. We show that for linear-quadratic optimal control problems, BASOC becomes equivalent to a snapshot method for initial condition balancing of the optimally controlled closed-loop realization. The framework is demonstrated on a nonlinear three-dimensional system where POD and balanced truncation are known to perform poorly, and on the nonlinear complex Ginzburg-Landau equation to demonstrate feasibility for larger-scale systems.
cs.LG
Wind farm layout optimization becomes increasingly difficult as turbine count grows, especially when high-fidelity aerodynamic models are used. Conventional Kriging-based Bayesian optimization often suffers from low sample efficiency in high-dimensional layout spaces because sparse training data provide limited domain coverage. We develop a Graph-informed Kriging framework that embeds reusable aerodynamic interaction information into the surrogate mean function. The wind farm is represented as a directed aerodynamic graph, where pairwise turbine interactions defined in a low-dimensional relative-coordinate space are assembled into a farm-scale mean. This structured mean captures the dominant variation in annual energy production (AEP), allowing the Gaussian process to model only the residual structure and uncertainty. Numerical experiments show that Graph-informed Kriging consistently improves AEP prediction accuracy over ordinary and generic-trend Kriging under sparse-data conditions, with increasing benefits as layout dimension and wake-interaction strength grow. Embedded in Bayesian optimization, it substantially improves sample efficiency and achieves competitive solutions for the IEA Wind Task 37 benchmark with about 280 AEP evaluations. In a controlled 16-turbine case, it also outperforms ordinary and multi-fidelity Kriging under the same high-fidelity evaluation budget. The framework is further extended to a 48-turbine wind farm, demonstrating applicability to large-scale CFD-based layout optimization. These results show that Graph-informed surrogate modeling can enable more efficient use of high-fidelity aerodynamic models for reliable large-scale wind farm layout design.
cs.CE
Many-query applications such as design optimization, uncertainty quantification and digital twins call for repeated full-field predictions from expensive partial differential equation (PDE) simulations. Grassmannian reduced-order models interpolate parameter-dependent subspaces but commonly vectorize the spatial field, and the ambient dimension then grows with the product of the grid sizes. Tensor-based models preserve the multiway structure but often use shared mode-wise bases or a fixed core across parameters. We propose a Tucker-based surrogate that keeps the spatial directions and time as separate modes. The factor subspaces are interpolated on a product Grassmann manifold, Procrustes alignment places the Tucker cores in one coordinate system, and a truncated singular value decomposition models their variation. Across five PDE benchmarks in two and three spatial dimensions, including public computational fluid dynamics data, the interpolation size is reduced by factors of 13 to 482 and the stored model by factors of 13 to 464 relative to a single-subspace Grassmannian surrogate with the same local interpolation. Cached queries run 3.3 to 8.5 times faster, the mean errors over ten resampled splits differ by at most 0.22 percentage points (0.36 when every model choice is re-selected inside each split), and the prediction-error terms of the two methods differ by less than 0.1 percentage points. The remaining accuracy differences are consistent with the larger representation error of the Tucker format. In a three-dimensional grid study the interpolation-size ratio rises to 2,260 and the query speedup to 11.9 at 64^3.
cs.LG
Generative machine learning provides an end-to-end framework for aerodynamic design optimization by generating diverse aerodynamic configurations conditioned on target performance. However, the generated designs may violate geometric constraints or deviate from the prescribed aerodynamic performance. Therefore, we propose a Condition-Enhanced Generative Adversarial Network (CEGAN) that embeds geometric and aerodynamic information into the generative process. A condition-informed loss function is developed by combining a geometry-guided feasibility penalty with an aerodynamic performance penalty evaluated by surrogate models trained on the same dataset. Additionally, an output filtering mechanism discards infeasible designs using these surrogate models, ensuring compliance with prescribed conditions. The method is demonstrated on aircraft configurations similar to the Cessna Citation X. Each configuration is represented by 28 shape parameters, whose aerodynamic performance is evaluated using OpenVSP. Interpolation and extrapolation tests are conducted for different network architectures and conditioning strategies. Compared with the baseline model, CEGAN reduces the average generation error by at least 36% for interpolated conditions and by at least 46% under extreme extrapolation conditions, while successfully producing diverse and physically feasible designs that extend beyond the training distribution. Therefore, the proposed CEGAN framework offers a robust and accurate tool for rapid aerodynamic configuration exploration during the conceptual design phase.
cs.LG
Computational Fluid Dynamics (CFD) is essential in contemporary engineering design, yet it remains expensive to compute and can take thousands of CPU/GPU hours or more for a single high-fidelity simulation. Recent developments in machine learning for scientific modeling have led to powerful surrogate frameworks that can approximate flow fields and physical quantities by using a fraction of the computational cost. However, most of these methods require largescale homogeneous datasets or simple geometries, exhibiting severe generalization gaps when applied to complex and data-scarce configurations. To address this, we propose the Factorized Geometry Convolutional Neural Operator (FG-ConvNO) for robust geometry-aware propeller pressure prediction under limited training data. We introduce geometryaware encoding, efficient interpolation-based decoding, and auxiliary global quantity supervision. In addition, we incorporate DISCO blocks that use discrete continuous convolutions to make the model discretization agnostic. In our experiments, FG-ConvNO achieves the highest accuracy for both surface pressure and global thrust prediction on a procedurally generated propeller CFD dataset. Geometry-aware encoding and architectural simplification improve stability, while random vertex subsampling enhances robustness. Overall, adding geometric priors to grid-based convolutional operators reduces overfitting on complex propeller geometries and improves generalization in low-data regimes.
physics.ao-ph
Computational Fluid Dynamics (CFD) is increasingly used to resolve wind, heat, and pollution patterns in cities with high fidelity but high computational cost. Thermal effects and vegetation are often neglected despite being important drivers, yet their influence may be masked by variability from other uncertain inputs. This work determines how precisely model parameters and boundary conditions must be known for these effects to govern output uncertainty in the velocity, temperature, and concentration fields of a two-dimensional street canyon. A global Sobol analysis of 24 parameters is made tractable by a surrogate trained on Reynolds-Averaged Navier–Stokes (RANS) simulations, combining principal component analysis (PCA) with multilayer perceptrons (MLPs). Two ventilation regimes assess the variance due to thermal and vegetation parameters as uncertainty in all other inputs is progressively reduced. At typical uncertainty levels, turbulence closure constants and inflow turbulence profiles govern variability in all three fields in both regimes. Under weak ventilation, solar forcing becomes the main source of temperature variability once the remaining uncertainties are halved, and of concentration variance once they are reduced to a quarter (about for the dominant closure constant and for the emission rate). In contrast, vegetation contributes significantly at typical uncertainty levels. Under strong ventilation, however, the canopy never overtakes the closure constants, even when the dominant one is known to . Spatially, both effects peak at road level and along canyon walls, which influence outdoor exposure and indoor infiltration, suggesting that high-resolution exposure studies should account for them.
eess.IV
4D Flow MRI promises opportunities for non-invasive mapping of neurovascular hemodynamics, but its clinical utility faces trade-offs between resolution, acquisition time, and noise. Deep learning-based super-resolution offers a post-scan solution, but supervised approaches require paired or domain-specific data, while patient-specific physics-informed models can be computationally demanding. This creates a need for unsupervised reconstruction models that can adapt to individual acquisitions without paired high-resolution training data, while remaining robust across anatomy, disease severity, and acquisition quality. To address this, we introduce a spectrally tuned implicit neural representation (INR) framework for intracranial 4D Flow MRI, representing the velocity field as a continuous coordinate-based neural field using WIRE activation functions with tunable spectral bias. Data-driven and physics-informed variants were evaluated in nine cerebrovascular cases spanning healthy controls and moderate-to-severe intracranial atherosclerotic disease, using CFD-derived high-resolution reference fields, MR-realistic synthetic low-resolution inputs, and direct fitting to acquired in-vivo data. Cohort-optimal spectral-bias settings generalized across subjects with only small increases in relative error compared with subject-specific optima, reducing the need for case-specific hyperparameter tuning. Physics-informed regularization provided modest additional accuracy but required substantially longer optimization, whereas the data-driven INR achieved near-comparable reconstruction performance within minutes. The framework also remained robust to acquisition-realistic noise and moderate VENC-dependent aliasing. These findings establish spectrally optimized INRs as a flexible unsupervised approach for mitigating acquisition-related trade-offs in intracranial 4D Flow MRI.
physics.comp-ph
High-fidelity Reynolds-averaged Navier–Stokes (RANS) simulations remain a standard tool for aerodynamic analysis, yet each solve on an unstructured mesh is computationally expensive, making many-query tasks such as design exploration, optimization, and multi-condition evaluation costly. Neural operators provide mesh-flexible surrogates for mapping geometry and operating conditions to flow fields; however, in many existing architectures, global parameters such as the freestream state and design variables are concatenated with pointwise inputs, leaving their influence on intermediate features implicit. This paper proposes CoPiT, a \textbf{Co}nditional \textbf{P}osition-induced \textbf{T}ransformer that builds on the Position-induced Transformer (PiT) as its geometric backbone and injects a global condition vector through multi-stage feature-wise linear modulation (FiLM), thereby making parametric modulation explicit while preserving geometry-based attention weights. The model is evaluated on two-dimensional AirfRANS airfoil fields, including scarce-data and extrapolation regimes, and on three-dimensional BlendedNet++ blended-wing-body surfaces. On the AirfRANS full-data test set, CoPiT reduces surface and volume losses by approximately 54% and 24% relative to Transolver, respectively, and achieves the lowest overall normalized test MSE among the compared baselines on BlendedNet++. Matched ablations show that FiLM conditioning generally improves upon input concatenation, while the preferred placement is task-dependent. CoPiT thus provides a practical operator-learning surrogate for parametric aerodynamics on unstructured meshes.
eess.SY
Model predictive control (MPC) has been applied to wind-turbine regulation with markedly different internal prediction models, ranging from linearized low-order turbine models to nonlinear, aeroelastic, floating, and data-driven predictors. This review organizes the literature primarily by the model propagated by the controller and treats model order, predictor representation, MPC formulation, control objective, and validation plant as separate descriptors. Primary studies show that low-order linear or piecewise-affine predictors are attractive for constrained power and speed regulation, whereas flexible drivetrain, structural, and floating models are needed when drivetrain twist, tower motion, blade loads, or platform dynamics are explicit control quantities. Nonlinear and economic MPC studies further demonstrate coordinated pitch/torque control across broad operating regions, while preview, repetitive, and data-driven formulations address wind prediction, periodic loads, and model mismatch. Particular attention is given to aerodynamic pitch dependence: variable-pitch turbine dynamics can be non-affine with respect to applied pitch, and the term NMPC alone does not prove that the original nonlinear pitch map is retained inside the optimizer. The review therefore adopts a conservative, equation-based classification for direct nonlinear applied-pitch propagation. Here, direct nonlinear applied-pitch propagation means that the applied pitch at each prediction step is evaluated inside the original nonlinear aerodynamic map, rather than replacing that dependence by a linearized, scheduled, piecewise-affine, or learned surrogate. The literature supports a remaining integration challenge at the intersection of flexible drivetrain prediction, nonlinear aerodynamic pitch treatment, explicit constraints and load objectives, and validated adaptive or learned model correction.
cs.CE
When the parameter of a PDE surrogate is the flow domain itself, the key/value set of an attention model must encode that domain. We present CARS (Coronary Artery Rapid Surrogate), a learned surrogate for the finite-volume solution map of the steady incompressible Navier-Stokes equations on coronary lumens built from image-derived centerlines. CARS maps a centerline with per-point lumen radius and a scalar inlet flow rate to pressure and velocity at arbitrary query points. The attention focal set is the case's own centerline, so geometry fusion costs rather than in the number of evaluation points. Trained per territory on OpenFOAM solutions with synthetic focal lesions, CARS reaches relative errors of (pressure/velocity) on left-coronary and on right-coronary test sets, in ~ms per case against a solver wall-clock of -~s. It outperforms four parameter-matched neural operators, and accuracy saturates eightfold below the operating focal count. Fractional flow reserve derived from the predicted pressure matches the CFD label at Pearson and reproduces the decision in of held-out cases. The predicted velocity does not satisfy continuity closely enough for derived quantities, and the weights are territory-specific.
physics.flu-dyn
This paper presents a grey-box framework for a robust deterministic, real-time prediction of coupled surge-heave-pitch motion in waves. The approach combines a physics-based online system identification with a nonlinear pseudo-spectral potential flow solver. Rather than prescribing a complete hydrodynamic model from captive model tests or relying on a black-box surrogate, three coupled compact, dynamics equations are identified with constrained ridge-regression of a short time-history of free-running body motion data in waves. The PSPF solver is then used to provide the future incident wave at the body's location. The PSPF and dynamic system models are coupled for continuous real-time future motion prediction in a receding horizon architecture, while the model coefficients are re-identified online to enable robust prediction to changing wave-conditions. This novel framework is demonstrated based on CFD data that has been validated for a freely floating two-dimensional rectangular box in waves. The results demonstrate a feasible framework for accurate motion forecasting up to around eight wavelengths into the future utilising data that can be gathered onboard a ship in real operations. Furthermore, it is shown that the framework is robust to changing sea-states and agnostic to underlying model geometry.
physics.ins-det
4H-SiC low gain avalanche detectors (LGADs) combine internal gain with the high carrier saturation drift velocity, thermal stability, and low leakage current of SiC. AC-coupled readout (AC-LGAD) adds position information to the precise timing, which enables 4D tracking. This work reports the first experimental demonstration of a 4H-SiC AC-LGAD. Devices with a ~mm active area and a array of AC-coupled pads at a pitch of 500~m were fabricated with two gain-layer doping concentrations, and ~cm, and characterized with a 375~nm ultraviolet transient current technique (UV-TCT) scanning setup. The hit position and time were reconstructed with the conventional discretized positioning circuit (DPC) and constant fraction discrimination (CFD) methods. They were also reconstructed with a transformer-based neural network, which processes the full waveforms of the four pads through a shared encoder and formulates timing as a per-sample segmentation task. With DPC and CFD, the best position and time resolutions are about 10~m and 20~ps. The neural network mitigates the nonlinear distortions near the device edges and improves the position resolution to below 5~m and the time resolution to about 15~ps, improvements of more than 60% and 30%, respectively. The higher-doped device reaches a high signal-to-noise ratio at lower bias and keeps good performance at low laser intensities. These results demonstrate the potential of 4H-SiC AC-LGADs for 4D tracking in future collider experiments.