In the present work, under some di¤erentiability conditions on the potential functions , we …rst reduce the inverse scattering problem (ISP) for the polynomial pencil of the Scroedinger equation to the corresponding ISP for the generalized matrix Scrödinger equation . Then ISP will be solved in analogy of the Marchenko method. We aim to establish an e¤ective algorithm for uniquely reconstructin...

in this paper the density and temperature dependencies of surface tension are investigated. using the lennard-jones (12,6), as an effective pair interaction potential, a linear expression is derived for isotherms of g /r2  versus r2 for some normal and chlorofluorocarbons (cfcs) fluids, where  is surface tension and r = 1/v is molar density. the linearity behavior of the derived equation is wel...

It is well known that the Schrödinger equation is only suitable for the particle in common potential V (~r, t). In this paper, a general Quantum Mechanics is proposed, where the Lagrangian is the general form. The new quantum wave equation can describe the particle which is in general potential V (~r, ~̇r, t). We think these new quantum wave equations can be applied in many fields. PACS numbers:...

There are various ways to impose a potential variation in time, and subsequently a series of voltammetric methods. Cyclic voltammetry relies on linearly scanning the potential in time, observing a triangular waveform variation [1-3]. Differential pulse voltammetry is based on two samplings of the current intensity for each potential pulse: one measurement before applying the pulse, and the seco...

Effectively incorporating external advice is an important problem in reinforcement learning, especially as it moves into the real world. Potential-based reward shaping is a way to provide the agent with a specific form of additional reward, with the guarantee of policy invariance. In this work we give a novel way to incorporate an arbitrary reward function with the same guarantee, by implicitly...

Scaling Reinforcement Learning (RL) to real-world problems with continuous state and action spaces remains a challenge. This is partly due to the reason that the optimal value function can become quite complex in continuous domains. In this paper, we propose to avoid learning the optimal value function at all but to use direct policy search methods in combination with model-based RL instead.