11![Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control Yangchen Pan 1 2 Amir-massoud Farahmand 3 2 Martha White 1 Saleh Nabi 2 Piyush Grover 2 Daniel Nikovski 2 Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control Yangchen Pan 1 2 Amir-massoud Farahmand 3 2 Martha White 1 Saleh Nabi 2 Piyush Grover 2 Daniel Nikovski 2](https://www.pdfsearch.io/img/632d8dacc8dcb49a4250f624efc91460.jpg) | Add to Reading ListSource URL: sologen.netLanguage: English - Date: 2018-06-07 18:37:02
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12![New York Journal of Mathematics New York J. Math. 17a–244. The Riccati differential equation and a diffusion-type equation Erwin Suazo, Sergei K. Suslov New York Journal of Mathematics New York J. Math. 17a–244. The Riccati differential equation and a diffusion-type equation Erwin Suazo, Sergei K. Suslov](https://www.pdfsearch.io/img/34e5e979791ab8e3893bdda18e0ac980.jpg) | Add to Reading ListSource URL: nyjm.albany.eduLanguage: English - Date: 2011-01-27 18:27:14
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13![At). Then, integration of the differential equation from the initial condition Y„-x 923 can be performed to determine the value at Yn. The solution can be determined tmuumMsMMimmm using the integrating factor or Lapl
At). Then, integration of the differential equation from the initial condition Y„-x 923 can be performed to determine the value at Yn. The solution can be determined tmuumMsMMimmm using the integrating factor or Lapl](https://www.pdfsearch.io/img/b07256dde97c926b792095c5cdd734c4.jpg) | Add to Reading ListSource URL: www.pc-education.mcmaster.caLanguage: English - Date: 2017-10-16 14:31:39
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14![ON A FUNCTIONAL-DIFFERENTIAL EQUATION ARISING FROM A TRAFFIC FLOW MODEL∗ REINHARD ILLNER AND GEOFFREY MCGREGOR† Abstract. We provide a derivation in the context of a traffic flow model, and both analytical and numeri ON A FUNCTIONAL-DIFFERENTIAL EQUATION ARISING FROM A TRAFFIC FLOW MODEL∗ REINHARD ILLNER AND GEOFFREY MCGREGOR† Abstract. We provide a derivation in the context of a traffic flow model, and both analytical and numeri](https://www.pdfsearch.io/img/123b1919a407d60bfb6ea7558340e039.jpg) | Add to Reading ListSource URL: www.math.uvic.caLanguage: English - Date: 2012-01-26 14:00:15
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15![Variation of Parameters The general solution to a second order non- homogeneous differential equation can be reduced to solving 2 first order differential equations. This differs from reduction of order in that we have 2 Variation of Parameters The general solution to a second order non- homogeneous differential equation can be reduced to solving 2 first order differential equations. This differs from reduction of order in that we have 2](https://www.pdfsearch.io/img/8f27c2d2dcca4045ddde0e8eae981207.jpg) | Add to Reading ListSource URL: calculus7.comLanguage: English - Date: 2009-08-11 13:21:53
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16![The Evans function: An example Consider the scalar partial differential equation (PDE) ut = uxx − u + u3 , which has the stationary solution u(x, t) = q(x) := u ∈ R, The Evans function: An example Consider the scalar partial differential equation (PDE) ut = uxx − u + u3 , which has the stationary solution u(x, t) = q(x) := u ∈ R,](https://www.pdfsearch.io/img/e68b4d813e09c7853f3612fb8125281e.jpg) | Add to Reading ListSource URL: www.dam.brown.eduLanguage: English - Date: 2010-12-20 12:48:30
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17![In order to solve this differential equation you look at it till a solution occurs to you. George Pólya In order to solve this differential equation you look at it till a solution occurs to you. George Pólya](https://www.pdfsearch.io/img/6e1ff9825f3bc10f4ba011564834bebe.jpg) | Add to Reading ListSource URL: members.loria.fr- Date: 2018-03-14 07:02:09
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18![Efficient Bayesian estimation and uncertainty quantification in ordinary differential equation models Efficient Bayesian estimation and uncertainty quantification in ordinary differential equation models](https://www.pdfsearch.io/img/6bb09311d75c887e0098ecb15e5c1882.jpg) | Add to Reading ListSource URL: arxiv.org- Date: 2016-02-22 20:42:34
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19![Bayesian inference for higher order ordinary differential arXiv:1505.04242v1 [math.ST] 16 May 2015 equation models Prithwish Bhaumik and Subhashis Ghosal Bayesian inference for higher order ordinary differential arXiv:1505.04242v1 [math.ST] 16 May 2015 equation models Prithwish Bhaumik and Subhashis Ghosal](https://www.pdfsearch.io/img/d3756396b24d9f463b1534da9796279f.jpg) | Add to Reading ListSource URL: arxiv.org- Date: 2015-05-18 20:46:46
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20![DiffLQN: Differential Equation Analysis of Layered Queuing Networks Tabea Waizmann Mirco Tribastone DiffLQN: Differential Equation Analysis of Layered Queuing Networks Tabea Waizmann Mirco Tribastone](https://www.pdfsearch.io/img/7d0c799309f8d096919eb95d3069dce7.jpg) | Add to Reading ListSource URL: dl.dropboxusercontent.com |
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