FusionFactory: Unified Fusion Architectures

Updated 16 July 2025

FusionFactory is an integrative concept that combines heterogeneous codes, models, and data modalities to achieve robust performance and enhanced adaptability.
It employs advanced methodologies such as parallel coupling, multi-fidelity synthesis, and adaptive expert fusion to optimize computational efficiency and accuracy.
Applications span plasma simulation, autonomous systems, and industrial optimization, demonstrating validated improvements in fidelity, scalability, and robustness.

FusionFactory is an integrative concept denoting systematic frameworks and architectures that enable robust fusion across heterogeneous components, codes, models, data modalities, and expert subsystems. In high-performance computing, applied machine learning, plasma science, autonomous systems, and industrial optimization, FusionFactory refers to the family of software, algorithmic, or workflow solutions designed to combine the strengths of disparate tools or knowledge sources—yielding enhanced fidelity, efficiency, and adaptability. Across recent literature, this concept finds distinctive manifestations in fields as diverse as fusion energy simulation, deep learning model integration, sensor-based perception, operator kernel compilation, expert model orchestration, and domain-specific data fusion. The following sections synthesize leading technical paradigms and implementations underpinning FusionFactory, highlighting their architectural patterns, mathematical formulations, and validated impact.

1. Parallel Coupling of Physics Codes and Simulation Frameworks

One central domain for FusionFactory is the orchestration of multi-physics codes to simulate complex systems. Notably, the FACETS framework exemplifies this paradigm in fusion plasma modeling by enabling parallel coupling of independent simulation components—such as core transport, edge transport, turbulence, and source models—within a unified executable (1004.1611). FACETS imposes a component-based architecture, standardizing interfaces for initialization, time advancement, data exchange (e.g., getDouble, setDouble), and finalization. Domain decomposition and dynamic processor allocation allow components to run in parallel subsets of the available resources, exchanging critical surface data (for example, “heatFlux” and “temperature”) with low-latency MPI communication.

The mathematical coupling often turns on solving one-dimensional transport equations,

$\frac{\partial n}{\partial t} = -\nabla \cdot \Gamma,$

where $n$ is the density and $\Gamma$ is a flux from embedded models. Numerical updaters, such as implicit Crank–Nicolson schemes,

$u_i^{n+1} = u_i^n + \frac{\Delta t}{2} \left[ F(u_{i+1}^{n+1} - 2u_i^{n+1} + u_{i-1}^{n+1}) + F(u_{i+1}^n - 2u_i^n + u_{i-1}^n) \right],$

are modular and pluggable within the framework. Validation on experimental tokamak data has shown that FACETS reproduces observed plasma profile evolution and can robustly handle component solver failures via rollback mechanisms, yielding improved fidelity over monolithic or loosely coupled alternatives.

2. Integrated Design Platforms and Multi-Fidelity Model Synthesis

FusionFactory also denotes advanced integrated design environments exemplified by frameworks such as FUSE (Fusion Synthesis Engine) (2409.05894). FUSE unifies first-principle solvers, machine learning surrogates, and reduced-order models for comprehensive design and optimization of fusion power plants. Its modular structure represents all components as “actors” acting on a centralized data dictionary structured by an extended IMAS ontology. FUSE supports hierarchies of model fidelity—for instance, switching between extended-Solov’ev analytic equilibrium, high-fidelity solvers (TEQUILA), and machine-learning-accelerated turbulence models (e.g., TGLF-NN).

The separation of physical timescales ( $\tau_\text{equilibrium} \ll \tau_\text{transport} \ll \tau_\text{resistive}$ ) allows switching between steady-state and dynamic simulation modes. Multi-objective optimization is performed via population-based metaheuristics (with objectives like capital cost, safety margins $q_{95}$ , or tritium breeding ratio), yielding Pareto-fronts that span the trade-off space between cost and performance. FUSE’s adoption of Julia enables in-memory coupling, multi-threading, and rapid prototyping. Its open-source nature and extensibility foster collaborative model development and reproducibility.

3. Adaptive Model and Expert Fusion Algorithms

FusionFactory encompasses algorithmic frameworks for combining machine learning model predictions, parameters, or internal representations. The “Fusion of Experts” (FoE) formalism presents one approach: given complementary domain expert models, a trainable “fuser” is trained on validation data to output superior predictions by aggregating concatenated expert outputs (2310.01542). The FoE objective, in the discriminative case, can be written as: $\min_\theta \sum_{k} \sum_{i} \ell\left(F_\theta\left([f_1(x_i^k),\dots,f_K(x_i^k)]\right), y_i^k\right),$ where $f_k$ are expert predictions and $F_\theta$ is the fuser. In generative tasks, the approach adapts by embedding generated tokens and choosing the closest to a reference.

Variants such as the “frugal” fusion framework adaptively select which experts to query (incurring minimal cost while maintaining accuracy), using an estimated loss function that balances prediction benefit and cost. Across image classification, sentiment analysis, and summarization, FoE has demonstrated substantial improvements, approaching “oracle” expert selection performance while scaling expert invocation costs.

Parameter and neuron-centric fusion represent another thrust, where algorithms such as AutoFusion (2410.05746) use unsupervised, end-to-end optimization to learn permutations for aligning and merging network parameters via differentiable Sinkhorn operators; and neuron-centric fusion (2507.00037) aligns groups of neurons (possibly with learned importance scores) to interpolate intermediate representations, robust to permutation and training data heterogeneity. Both approaches outperform naïve weight interpolation and static alignment, particularly in non-IID, multi-task, and zero-shot regimes.

4. Automated Kernel and Operator Fusion in High-Performance Computing

The FusionFactory concept further manifests in compiler-level operator fusion for performance-critical workloads. Frameworks such as HFAV (1710.08774) and MCFuser (2506.22169) automate the transformation of loop-based or kernel-based computations into tightly fused, vectorized, and memory-efficient kernel chains. HFAV expresses computation as dataflow DAGs and iteration nests, applying two-level fusion guided by rank and dataflow ordering; it contracts storage with reuse analysis and emits code with in-place rotating buffers for maximized cache reuse and SIMD vectorization.

MCFuser advances this by exhaustively searching a high-level tiling expression space for chains of memory-bound, compute-intensive operators. It uses directed acyclic graph analysis to remove redundant memory accesses, applies analytical performance modeling, and deploys heuristic search to rapidly converge on optimal kernel fusions. Empirical results indicate speedups up to 5.9× with tuning time reduced over seventy-fold compared to leading compilers.

In robotic perception and autonomous systems, FusionFactory emerges as cross-modal fusion frameworks such as ZFusion (2504.03438), which fuses 4D radar (sparse but robust) and camera (dense but ambiguous) features for 3D object perception. The FP-DDCA (Feature Pyramid-Double Deformable Cross Attention) module fuses feature maps at multiple scales, employing a two-pass deformable attention scheme: $\text{head}_h = \sum_{n=1}^{N} A_{h,q,n,i\rightarrow(1-i)} \cdot \left[W_{h,i} \cdot x_{1-i}(p'_{h,q,n,i\rightarrow(1-i)})\right],$ with $p'_{h,q,n,i\rightarrow(1-i)} = p_{q,i} + \Delta p_{h,q,n,i\rightarrow(1-i)}$ . ZFusion incorporates a depth-context-split for robust BEV view transformation under sensor uncertainty. It demonstrates 74.38% mAP (region of interest) on the VoD dataset, approaching LiDAR-class performance at camera-radar cost.

6. Automated Architecture and Fusion Design Search

Another technical direction is the automated learning of optimal fusion architectures. OptFusion (2411.15731) jointly searches the space of fusion connections and operations within deep CTR models using architecture parameters $\alpha_{ij}$ (for connections) and $\beta$ (for operation selection), optimizing both within a single one-shot training phase: $\min_{\Theta, \{\alpha, \beta\}} \mathcal{L}_{\mathcal{D}}(\Theta, \{\alpha, \beta\}),$ where candidate fusion operators include ADD, PROD, CONCAT, and ATT. The “hard” variant selects the maximal $\beta$ per node to fix the fusion strategy, while the “soft” variant retains a weighted combination throughout. OptFusion demonstrates state-of-the-art AUC and LogLoss on standard CTR datasets, outperforming both hand-designed and NAS-searched baselines with greater efficiency.

7. FusionFactory in Benchmarking, Workforce, and Process-Property Modeling

FusionFactory is further seen in benchmark and workflow integration across both AI and scientific domains. FusionBench (2507.10540) is a large-scale routing benchmark spanning 14 tasks and 20 LLMs, enabling systematic evaluation of three-level fusion: query-level (router-based expert selection), thought-level (reuse of abstracted reasoning templates), and model-level (distillation of best responses). Experiments show that optimal fusion strategies differ by benchmark, but all consistently outperform single-model baselines.

In the workforce context, FusionFactory can be interpreted as a centralized, modular education and training platform that brings together stakeholders from industry, academia, and national labs to coordinate curriculum, internships, and upskilling strategies at scale (2501.03372).

In materials and manufacturing, FusionFactory is reflected in data fusion strategies for building foundation process-property models in additive manufacturing (2503.16667), where Gaussian Processes are trained with both single- and multi-task outputs, analyzing the impact of data augmentation, kernel lengthscales, and structured (versus uninformed) data fusion.

FusionFactory thus encompasses the architectural, algorithmic, and systemic methodologies required to realize effective fusion across software, hardware, organizational, and knowledge boundaries—spanning high-fidelity physical simulation, model and kernel compilation, multi-modal inference, declarative learning, and benchmark-driven evaluation. Each domain-specific instantiation extends the FusionFactory principle: flexible integration of heterogeneous sources to achieve high accuracy, scalability, and agility, validated by rigorous mathematical formulation, systematic benchmarking, and demonstrable improvement over monolithic or ad hoc approaches.