activity
20242026
collaborators

6 papers

physics.flu-dyn2026

Long-lived versus nocturnal stable atmospheric boundary layers: DNS characterisation, similarity theory, and regime classification

K. Chand, Cheng-Nian Xiao, Inanc Senocak

The stable atmospheric boundary layer (SABL) is broadly classified into two subtypes: the nocturnal SABL, driven primarily by surface cooling, and the long-lived SABL, in which amb…

physics.flu-dyn2026

Unsupervised simulation of incompressible flows with physics- and equality- constrained artificial neural networks

Qifeng Hu, Inanc Senocak

Physics-informed neural networks (PINNs) have shown promise for solving partial differential equations, yet their success in simulating incompressible flows at high Reynolds number…

cs.LG2025

Conditionally adaptive augmented Lagrangian method for physics-informed learning of forward and inverse problems

Qifeng Hu, Shamsulhaq Basir, Inanc Senocak

We present several key advances to the Physics and Equality Constrained Artificial Neural Networks (PECANN) framework, substantially improving its capacity to solve challenging par…

physics.flu-dyn2025

Dynamical stability and flow regimes in a stably stratified valley-shaped cavity heated from below

Patrick J. Stofanak, Cheng-Nian Xiao, Inanc Senocak

We investigate the three-dimensional stability of a stably stratified fluid in a valley-shaped cavity heated from below using linear stability analysis and direct numerical simulat…

physics.flu-dyn2024

Self-organization in a stably stratified, valley-shaped enclosure heated from below

Patrick J. Stofanak, Cheng-Nian Xiao, Inanc Senocak

We observe the spontaneous onset of three-dimensional motion from a quiescent, purely conductive state of a stably stratified fluid in a V-shaped enclosure heated from below, which…

cs.LG2024

Non-overlapping, Schwarz-type Domain Decomposition Method for Physics and Equality Constrained Artificial Neural Networks

Qifeng Hu, Shamsulhaq Basir, Inanc Senocak

We present a non-overlapping, Schwarz-type domain decomposition method with a generalized interface condition, designed for physics-informed machine learning of partial differentia…