4 citations · 7 across the 11 of their papers we have counts for
9 papers · 1 filter
On Embedding Design in Quantum Physics-Informed Neural Networks
Ban Q. Tran, Nahid Binandeh Dehaghani, Susan Mengel +2
Quantum physics-informed neural networks (QPINNs) solve partial differential equations (PDEs) by training parameterized quantum circuits against physics-based residuals, yet the ro…
Robust Fault Detection in Mechanical Multimodal Time Series via Self-Supervised Cross-Modal Reconstruction
Magnus Munk Jensen, Dorte Hammershøi, Rafał Wiśniewski +1
Fault detection is essential in industrial systems, enabling early identification of abnormal behaviour and improving safety, reliability, and operational efficiency. Modern system…
Rank-Dependent Error Bounds and Near-Optimality in Quantum Control via Hierarchical Tucker Surrogates
Nahid Binandeh Dehaghani, Rafal Wisniewski, Julian Berberich +1
We develop a certified finite-horizon quantum optimal-control framework based on fixed-rank Hierarchical Tucker (HT) surrogates. Under a uniform rank-dependent HT truncation-accura…
Fault detection on manifolds of nonlinear dynamical systems with dual autoencoders
Bulut Kuşkonmaz, Szymon Greś, Rafał Wiśniewski
Autoencoders are commonly used for unsupervised data-driven fault detection in nonlinear dynamical systems. Despite their widespread success and often favorable performance compare…
Tensor Network Methods for Advection-Diffusion-Reaction Systems Using Quantum-Inspired Representations
Nahid Binandeh Dehaghani, Rafal Wisniewski, A. Pedro Aguiar
We present a quantum-inspired tensor-network framework for solving advection-diffusion-reaction (ADR) partial differential equations. Discretized solution fields are encoded as mat…
A Variational Surrogate Approach to Finite-Horizon Quantum Control via Hardware-Efficient Ansatz
Nahid Binandeh Dehaghani, Rafal Wisniewski, A. Pedro Aguiar
We present a variational quantum framework for finite-horizon quantum control based on hardware-efficient ansätze. The objective is to steer a quantum system from a given initial s…
A QPINN Framework with Quantum Trainable Embeddings for the Lid-Driven Cavity Problem
Nahid Binandeh Dehaghani, Ban Q. Tran, Susan Mengel +2
The steady incompressible Navier--Stokes equations pose significant computational challenges due to their nonlinear convective terms and pressure--velocity coupling. Physics-inform…
Quantum-Assisted Trainable-Embedding Physics-Informed Neural Networks for Parabolic PDEs
Ban Q. Tran, Nahid Binandeh Dehaghani, Rafal Wisniewski +2
Physics-informed neural networks (PINNs) have emerged as a powerful framework for solving partial differential equations (PDEs) by embedding governing physical laws directly into t…
A Trainable-Embedding Quantum Physics-Informed Framework for Multi-Species Reaction-Diffusion Systems
Ban Q. Tran, Nahid Binandeh Dehaghani, A. Pedro Aguiar +2
Physics-informed neural networks (PINNs) and hybrid quantum-classical extensions provide a promising framework for solving partial differential equations (PDEs) by embedding physic…