arXiv subjects

Aimé Fournier

Publications and source records attributed to Aimé Fournier.

4 recordsLinked to original sources

arXiv2026

Structural Alignment for Reliable Industrial AI: Bridging Physical Reality, Data, Models, and Human Intent

Artificial intelligence is increasingly deployed in critical industrial domains, including healthcare, energy grids, subsurface exploration, where failures can have severe consequences for human safety, system stability, and economic outcomes. Yet AI is still evaluated primarily through benchmark accuracy, a model-centric metric that fails to capture the structural complexity and risks of real-world deployment. We propose a framework that views industrial AI reliability as a problem of structural alignment across four interacting worlds: physical, representational, machine, and human cognitive. These worlds are connected through two interfaces: digitalization, linking physical reality to computational representations, and goal encoding, translating human cognition to the machine objectives. Together, they define the space of admissible solutions. We characterize the solution space through four attributes: existence, non-uniqueness, robustness, and interpretability and show how mismatches arise at interfaces and propagate across worlds to produce reliability failures. Applications to healthcare, energy grids, and subsurface exploration illustrate that although dominant failure modes differ across domains, for example, interpretability in healthcare, robustness in energy grids, and non-uniqueness in subsurface exploration, all originate from a shared structural mechanism. By shifting the focus from model-centric evaluation to system-level alignment, this framework offers a principled foundation for assessing and governing reliability in industrial AI systems.

cs.AI↗

arXiv2026

Spacetime triple wormhole

We describe a multi-neck spacetime wormhole with a simple metric tensor and a simple injective map without coordinate patching. An intra-universe, non-thin-shell, non-spherically-symmetric 3-neck spacetime wormhole is geometrically constructed by spherically inverting a 3-torus. We place the resulting Dupin hypercyclide in a synchronous reference frame. The three necks are arranged around a central point and satisfy topological and geometric spacetime wormhole definitions. Asserting this metric tensor as an exact solution of Einstein's field equations in global coordinates generates diagonal Ricci and stress-energy tensors, and a Riemann curvature tensor with only six nonzero entries. The local inertial frame at every point of the coordinate system is comoving with the triple wormhole. This non-vacuum solution answers affirmatively the question posed by Einstein and Rosen (1935) of whether or not multi-neck solutions exist. The wormhole solution contains negative energy density as is expected to hold the necks open; however, geodesic paths through each neck exist which encounter only positive energy density. The spatial manifold is a trivariate Dupin hypercyclide. The spherically inverted equal-radii 3-torus is unbounded, asymptotically flat and admits a global isothermal coordinate system that further simplifies the curvature tensors.

gr-qc↗

arXiv2018

Focused blind deconvolution

We introduce a novel multichannel blind deconvolution (BD) method that extracts sparse and front-loaded impulse responses from the channel outputs, i.e., their convolutions with a single arbitrary source. A crucial feature of this formulation is that it doesn't encode support restrictions on the unknowns, unlike most prior work on BD. The indeterminacy inherent to BD, which is difficult to resolve with a traditional L1 penalty on the impulse responses, is resolved in our method because it seeks a first approximation where the impulse responses are: "maximally white" -- encoded as the energy focusing near zero lag of the impulse-response auto-correlations; and "maximally front-loaded" -- encoded as the energy focusing near zero time of the impulse responses. Hence we call the method focused blind deconvolution (FBD). The focusing constraints are relaxed as the iterations progress. Note that FBD requires the duration of the channel outputs to be longer than that of the unknown impulse responses. A multichannel blind deconvolution problem that is appropriately formulated by sparse and front-loaded impulse responses arises in seismic inversion, where the impulse responses are the Green's function evaluations at different receiver locations, and the operation of a drill bit inputs the noisy and correlated source signature into the subsurface. We demonstrate the benefits of FBD using seismic-while-drilling numerical experiments, where the noisy data recorded at the receivers are hard to interpret, but FBD can provide the processing essential to separate the drill-bit (source) signature from the interpretable Green's function.

eess.SP↗

arXiv2018

Experimental Design of a Prescribed Burn Instrumentation

Observational data collected during experiments, such as the planned Fire and Smoke Model Evaluation Experiment (FASMEE), are critical for progressing and transitioning coupled fire-atmosphere models like WRF-SFIRE and WRF-SFIRE-CHEM into operational use. Historical meteorological data, representing typical weather conditions for the anticipated burn locations and times, have been processed to initialize and run a set of simulations representing the planned experimental burns. Based on an analysis of these numerical simulations, this paper provides recommendations on the experimental setup that include the ignition procedures, size and duration of the burns, and optimal sensor placement. New techniques are developed to initialize coupled fire-atmosphere simulations with weather conditions typical of the planned burn locations and time of the year. Analysis of variation and sensitivity analysis of simulation design to model parameters by repeated Latin Hypercube Sampling are used to assess the locations of the sensors. The simulations provide the locations of the measurements that maximize the expected variation of the sensor outputs with the model parameters.

physics.ao-ph↗